%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from mpl_toolkits.basemap import Basemap
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from scipy.stats import ttest_ind
pd.set_option('display.max_rows', 200)
pd.set_option('display.max_columns', 200)
We will analyse Fifa dataset for two years 2016 and 2020. It is obtained from Kaggle. There are several notebooks using this dataset in Kaggle. Good examples are: Notebook 1, Notebook 2 and Notebook 3. The 2016 data will be used for preliminary analysis and training a linear regression model. Then, we will try to predict players' value in 2020. This will not be a time series analysis, but rather an attempt to tell price based on changed characteristics of the players like age, club, abilities which can improve or deteriorate (in case of injury for example). The structure of the work is as following:
Most of the column names are self explanatory. I will mention only those which are not understandable for the general public. To start with - the group of: 'team_positions','ls', 'st', 'rs', 'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm', 'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb'.
These are abbreviations of players' positions on the field. Best they can be understood is by looking at the following picture.

players_16=pd.read_csv("data/players_16.csv",index_col="sofifa_id")
players_16.head()
| player_url | short_name | long_name | age | dob | height_cm | weight_kg | nationality | club | overall | potential | value_eur | wage_eur | player_positions | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | body_type | real_face | release_clause_eur | player_tags | team_position | team_jersey_number | loaned_from | joined | contract_valid_until | nation_position | nation_jersey_number | pace | shooting | passing | dribbling | defending | physic | gk_diving | gk_handling | gk_kicking | gk_reflexes | gk_speed | gk_positioning | player_traits | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | mentality_composure | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 158023 | https://sofifa.com/player/158023/lionel-messi/... | L. Messi | Lionel Andrés Messi Cuccittini | 28 | 1987-06-24 | 170 | 72 | Argentina | FC Barcelona | 94 | 95 | 111000000 | 550000 | RW, CF | Left | 5 | 4 | 4 | Medium/Low | Messi | Yes | NaN | #Speedster, #Dribbler, #FK Specialist, #Acroba... | RW | 10.0 | NaN | 2004-07-01 | 2018.0 | RW | 10.0 | 92.0 | 88.0 | 86.0 | 95.0 | 24.0 | 62.0 | NaN | NaN | NaN | NaN | NaN | NaN | Finesse Shot, Speed Dribbler (CPU AI Only), On... | 80-4 | 93-1 | 71 | 88-1 | 85 | 96 | 89 | 90 | 79+3 | 96 | 95-1 | 90 | 92-2 | 92-2 | 95 | 80 | 68-5 | 76-1 | 59-1 | 88 | 48 | 22 | 90-2 | 90 | 74 | NaN | 13-12 | 23+2 | 21+1 | 6 | 11 | 15 | 14 | 8 | 87+3 | 87+3 | 87+3 | 91+3 | 91+3 | 91+3 | 91+3 | 91+3 | 91+3 | 91+3 | 91+3 | 90+3 | 82+3 | 82+3 | 82+3 | 90+3 | 62+3 | 57+3 | 57+3 | 57+3 | 62+3 | 57+3 | 44+3 | 44+3 | 44+3 | 57+3 |
| 20801 | https://sofifa.com/player/20801/c-ronaldo-dos-... | Cristiano Ronaldo | Cristiano Ronaldo dos Santos Aveiro | 30 | 1985-02-05 | 185 | 80 | Portugal | Real Madrid | 93 | 93 | 85500000 | 475000 | LW, LM | Right | 5 | 4 | 5 | High/Low | C. Ronaldo | Yes | NaN | #Speedster, #Dribbler, #Distance Shooter, #Acr... | LM | 7.0 | NaN | 2009-07-01 | 2018.0 | ST | 7.0 | 92.0 | 93.0 | 80.0 | 91.0 | 33.0 | 78.0 | NaN | NaN | NaN | NaN | NaN | NaN | Power Free-Kick, Flair, Long Shot Taker (CPU A... | 82-1 | 95 | 86 | 81-1 | 87 | 93 | 88 | 77-2 | 72 | 91-1 | 91 | 93-1 | 90-3 | 92+2 | 62-1 | 94 | 94 | 87-2 | 79 | 93 | 62-1 | 29+5 | 93+2 | 81 | 85 | NaN | 22 | 31 | 23 | 7 | 11 | 15 | 14 | 11 | 91+3 | 91+3 | 91+3 | 90+3 | 91+3 | 91+3 | 91+3 | 90+3 | 88+3 | 88+3 | 88+3 | 88+3 | 80+3 | 80+3 | 80+3 | 88+3 | 64+3 | 60+3 | 60+3 | 60+3 | 64+3 | 60+3 | 52+3 | 52+3 | 52+3 | 60+3 |
| 9014 | https://sofifa.com/player/9014/arjen-robben/16... | A. Robben | Arjen Robben | 31 | 1984-01-23 | 180 | 80 | Netherlands | FC Bayern München | 90 | 90 | 56000000 | 250000 | RM, LM, RW | Left | 5 | 2 | 4 | High/Low | Normal | Yes | NaN | #Speedster, #Dribbler, #Distance Shooter, #Acr... | SUB | 10.0 | NaN | 2009-08-28 | 2017.0 | RW | 11.0 | 92.0 | 86.0 | 82.0 | 92.0 | 32.0 | 64.0 | NaN | NaN | NaN | NaN | NaN | NaN | Diver, Injury Prone, Avoids Using Weaker Foot,... | 80 | 85 | 51-1 | 85-1 | 86 | 93 | 86+1 | 83 | 74-2 | 90-2 | 92-1 | 92-1 | 91-2 | 91 | 91 | 86 | 61 | 76-2 | 65 | 90 | 47 | 39 | 89 | 84 | 80 | NaN | 29 | 26 | 26 | 10 | 8 | 11 | 5 | 15 | 84+3 | 84+3 | 84+3 | 89+3 | 88+3 | 88+3 | 88+3 | 89+3 | 88+3 | 88+3 | 88+3 | 87+3 | 80+3 | 80+3 | 80+3 | 87+3 | 65+3 | 60+3 | 60+3 | 60+3 | 65+3 | 59+3 | 47+3 | 47+3 | 47+3 | 59+3 |
| 167495 | https://sofifa.com/player/167495/manuel-neuer/... | M. Neuer | Manuel Neuer | 29 | 1986-03-27 | 193 | 92 | Germany | FC Bayern München | 90 | 90 | 58000000 | 250000 | GK | Right | 5 | 4 | 1 | Medium/Medium | Normal | Yes | NaN | NaN | GK | 1.0 | NaN | 2011-07-01 | 2019.0 | GK | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | 85.0 | 87.0 | 91.0 | 86.0 | 60.0 | 90.0 | GK Long Throw, 1-on-1 Rush | 15-10 | 13-12 | 25 | 48+6 | 11-14 | 16-9 | 14-11 | 11-14 | 47+6 | 31 | 58 | 61 | 43 | 88 | 35 | 25-17 | 78 | 44 | 83 | 16-9 | 29 | 30 | 12-13 | 70+20 | 37 | NaN | 10-15 | 10-15 | 11-14 | 85-3 | 87+2 | 91 | 90 | 86 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 176580 | https://sofifa.com/player/176580/luis-suarez/1... | L. Suárez | Luis Alberto Suárez Díaz | 28 | 1987-01-24 | 182 | 85 | Uruguay | FC Barcelona | 90 | 90 | 69000000 | 300000 | ST | Right | 5 | 4 | 4 | High/Medium | Normal | Yes | NaN | #Acrobat, #Clinical Finisher | ST | 9.0 | NaN | 2014-07-11 | 2019.0 | NaN | NaN | 83.0 | 88.0 | 79.0 | 87.0 | 42.0 | 79.0 | NaN | NaN | NaN | NaN | NaN | NaN | Diver, Beat Offside Trap, Flair, Technical Dri... | 77 | 90-1 | 77+2 | 82 | 87+2 | 88-2 | 86 | 84 | 64 | 91+2 | 88 | 78-1 | 86 | 91 | 60 | 88+4 | 69 | 86 | 76 | 85+3 | 78 | 41 | 91+3 | 84 | 85 | NaN | 30 | 45 | 38 | 27 | 25 | 31 | 33 | 37 | 87+3 | 87+3 | 87+3 | 87+3 | 88+3 | 88+3 | 88+3 | 87+3 | 86+3 | 86+3 | 86+3 | 85+3 | 79+3 | 79+3 | 79+3 | 85+3 | 67+3 | 65+3 | 65+3 | 65+3 | 67+3 | 64+3 | 58+3 | 58+3 | 58+3 | 64+3 |
players_16.shape
(14881, 103)
From the database we can see that some of the columns will not be necessary for the analysis. They are: "player_url","long_name" (we have "short_name"), "wage_eur". Usually, wage is directly related to value, so either we know both or try to guess them. Makes no sense to make prediction for one based on the knowledge of the other. For the same reason, "Release clause" should also be removed. It is determined together with the value. Anyway, in this dataset there is no info about it.
Let's now see some general information about the dataset.
players_16.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 14881 entries, 158023 to 11728 Columns: 103 entries, player_url to rb dtypes: float64(17), int64(10), object(76) memory usage: 11.8+ MB
players_16.isnull().sum().sort_values(ascending=False)
mentality_composure 14881 release_clause_eur 14881 nation_jersey_number 13871 nation_position 13871 loaned_from 13827 player_tags 13625 gk_diving 13263 gk_handling 13263 gk_kicking 13263 gk_reflexes 13263 gk_speed 13263 gk_positioning 13263 player_traits 8119 rcb 1618 ls 1618 dribbling 1618 shooting 1618 pace 1618 rs 1618 passing 1618 physic 1618 st 1618 rb 1618 lw 1618 rm 1618 lam 1618 cam 1618 ram 1618 lm 1618 lcm 1618 cm 1618 rcm 1618 lwb 1618 lf 1618 ldm 1618 cdm 1618 rdm 1618 rwb 1618 lb 1618 lcb 1618 cb 1618 rw 1618 defending 1618 rf 1618 cf 1618 joined 1225 team_position 171 team_jersey_number 171 contract_valid_until 171 skill_moves 0 short_name 0 long_name 0 age 0 dob 0 height_cm 0 weight_kg 0 nationality 0 club 0 potential 0 overall 0 work_rate 0 value_eur 0 wage_eur 0 player_positions 0 preferred_foot 0 international_reputation 0 weak_foot 0 body_type 0 real_face 0 skill_long_passing 0 attacking_crossing 0 power_strength 0 goalkeeping_reflexes 0 goalkeeping_positioning 0 goalkeeping_kicking 0 goalkeeping_handling 0 goalkeeping_diving 0 defending_sliding_tackle 0 defending_standing_tackle 0 defending_marking 0 mentality_penalties 0 mentality_vision 0 mentality_positioning 0 mentality_interceptions 0 mentality_aggression 0 power_long_shots 0 power_stamina 0 attacking_finishing 0 power_jumping 0 power_shot_power 0 movement_balance 0 movement_reactions 0 movement_agility 0 movement_sprint_speed 0 movement_acceleration 0 skill_ball_control 0 skill_fk_accuracy 0 skill_curve 0 skill_dribbling 0 attacking_volleys 0 attacking_short_passing 0 attacking_heading_accuracy 0 player_url 0 dtype: int64
There are some features which have Null values for quite a large amount of records. "mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags" have 91.6-100% missing data, so they can be removed. "gk_kicking", "gk_reflexes", "gk_speed", "gk_positioning", "gk_diving", "gk_handling" also have many null values but that is because the features are related to goal-keepers which are naturally much less than the field players. So we will keep them for now.
#Remove unnecessary columns
players_16=players_16.drop(["player_url","long_name","wage_eur","release_clause_eur","mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags"],axis=1)
players_16.head()
| short_name | age | dob | height_cm | weight_kg | nationality | club | overall | potential | value_eur | player_positions | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | body_type | real_face | team_position | team_jersey_number | joined | contract_valid_until | pace | shooting | passing | dribbling | defending | physic | gk_diving | gk_handling | gk_kicking | gk_reflexes | gk_speed | gk_positioning | player_traits | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 158023 | L. Messi | 28 | 1987-06-24 | 170 | 72 | Argentina | FC Barcelona | 94 | 95 | 111000000 | RW, CF | Left | 5 | 4 | 4 | Medium/Low | Messi | Yes | RW | 10.0 | 2004-07-01 | 2018.0 | 92.0 | 88.0 | 86.0 | 95.0 | 24.0 | 62.0 | NaN | NaN | NaN | NaN | NaN | NaN | Finesse Shot, Speed Dribbler (CPU AI Only), On... | 80-4 | 93-1 | 71 | 88-1 | 85 | 96 | 89 | 90 | 79+3 | 96 | 95-1 | 90 | 92-2 | 92-2 | 95 | 80 | 68-5 | 76-1 | 59-1 | 88 | 48 | 22 | 90-2 | 90 | 74 | 13-12 | 23+2 | 21+1 | 6 | 11 | 15 | 14 | 8 | 87+3 | 87+3 | 87+3 | 91+3 | 91+3 | 91+3 | 91+3 | 91+3 | 91+3 | 91+3 | 91+3 | 90+3 | 82+3 | 82+3 | 82+3 | 90+3 | 62+3 | 57+3 | 57+3 | 57+3 | 62+3 | 57+3 | 44+3 | 44+3 | 44+3 | 57+3 |
| 20801 | Cristiano Ronaldo | 30 | 1985-02-05 | 185 | 80 | Portugal | Real Madrid | 93 | 93 | 85500000 | LW, LM | Right | 5 | 4 | 5 | High/Low | C. Ronaldo | Yes | LM | 7.0 | 2009-07-01 | 2018.0 | 92.0 | 93.0 | 80.0 | 91.0 | 33.0 | 78.0 | NaN | NaN | NaN | NaN | NaN | NaN | Power Free-Kick, Flair, Long Shot Taker (CPU A... | 82-1 | 95 | 86 | 81-1 | 87 | 93 | 88 | 77-2 | 72 | 91-1 | 91 | 93-1 | 90-3 | 92+2 | 62-1 | 94 | 94 | 87-2 | 79 | 93 | 62-1 | 29+5 | 93+2 | 81 | 85 | 22 | 31 | 23 | 7 | 11 | 15 | 14 | 11 | 91+3 | 91+3 | 91+3 | 90+3 | 91+3 | 91+3 | 91+3 | 90+3 | 88+3 | 88+3 | 88+3 | 88+3 | 80+3 | 80+3 | 80+3 | 88+3 | 64+3 | 60+3 | 60+3 | 60+3 | 64+3 | 60+3 | 52+3 | 52+3 | 52+3 | 60+3 |
| 9014 | A. Robben | 31 | 1984-01-23 | 180 | 80 | Netherlands | FC Bayern München | 90 | 90 | 56000000 | RM, LM, RW | Left | 5 | 2 | 4 | High/Low | Normal | Yes | SUB | 10.0 | 2009-08-28 | 2017.0 | 92.0 | 86.0 | 82.0 | 92.0 | 32.0 | 64.0 | NaN | NaN | NaN | NaN | NaN | NaN | Diver, Injury Prone, Avoids Using Weaker Foot,... | 80 | 85 | 51-1 | 85-1 | 86 | 93 | 86+1 | 83 | 74-2 | 90-2 | 92-1 | 92-1 | 91-2 | 91 | 91 | 86 | 61 | 76-2 | 65 | 90 | 47 | 39 | 89 | 84 | 80 | 29 | 26 | 26 | 10 | 8 | 11 | 5 | 15 | 84+3 | 84+3 | 84+3 | 89+3 | 88+3 | 88+3 | 88+3 | 89+3 | 88+3 | 88+3 | 88+3 | 87+3 | 80+3 | 80+3 | 80+3 | 87+3 | 65+3 | 60+3 | 60+3 | 60+3 | 65+3 | 59+3 | 47+3 | 47+3 | 47+3 | 59+3 |
| 167495 | M. Neuer | 29 | 1986-03-27 | 193 | 92 | Germany | FC Bayern München | 90 | 90 | 58000000 | GK | Right | 5 | 4 | 1 | Medium/Medium | Normal | Yes | GK | 1.0 | 2011-07-01 | 2019.0 | NaN | NaN | NaN | NaN | NaN | NaN | 85.0 | 87.0 | 91.0 | 86.0 | 60.0 | 90.0 | GK Long Throw, 1-on-1 Rush | 15-10 | 13-12 | 25 | 48+6 | 11-14 | 16-9 | 14-11 | 11-14 | 47+6 | 31 | 58 | 61 | 43 | 88 | 35 | 25-17 | 78 | 44 | 83 | 16-9 | 29 | 30 | 12-13 | 70+20 | 37 | 10-15 | 10-15 | 11-14 | 85-3 | 87+2 | 91 | 90 | 86 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 176580 | L. Suárez | 28 | 1987-01-24 | 182 | 85 | Uruguay | FC Barcelona | 90 | 90 | 69000000 | ST | Right | 5 | 4 | 4 | High/Medium | Normal | Yes | ST | 9.0 | 2014-07-11 | 2019.0 | 83.0 | 88.0 | 79.0 | 87.0 | 42.0 | 79.0 | NaN | NaN | NaN | NaN | NaN | NaN | Diver, Beat Offside Trap, Flair, Technical Dri... | 77 | 90-1 | 77+2 | 82 | 87+2 | 88-2 | 86 | 84 | 64 | 91+2 | 88 | 78-1 | 86 | 91 | 60 | 88+4 | 69 | 86 | 76 | 85+3 | 78 | 41 | 91+3 | 84 | 85 | 30 | 45 | 38 | 27 | 25 | 31 | 33 | 37 | 87+3 | 87+3 | 87+3 | 87+3 | 88+3 | 88+3 | 88+3 | 87+3 | 86+3 | 86+3 | 86+3 | 85+3 | 79+3 | 79+3 | 79+3 | 85+3 | 67+3 | 65+3 | 65+3 | 65+3 | 67+3 | 64+3 | 58+3 | 58+3 | 58+3 | 64+3 |
players_16.shape
(14881, 94)
players_16.describe().transpose()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| age | 14881.0 | 2.493045e+01 | 4.603521e+00 | 16.0 | 21.00 | 24.0 | 28.00 | 45.0 |
| height_cm | 14881.0 | 1.811025e+02 | 6.701927e+00 | 155.0 | 176.00 | 181.0 | 186.00 | 204.0 |
| weight_kg | 14881.0 | 7.542820e+01 | 6.933863e+00 | 50.0 | 70.00 | 75.0 | 80.00 | 110.0 |
| overall | 14881.0 | 6.565043e+01 | 7.095894e+00 | 44.0 | 61.00 | 66.0 | 70.00 | 94.0 |
| potential | 14881.0 | 7.028869e+01 | 6.293664e+00 | 44.0 | 66.00 | 70.0 | 75.00 | 95.0 |
| value_eur | 14881.0 | 1.766313e+06 | 4.172073e+06 | 0.0 | 250000.00 | 575000.0 | 1400000.00 | 111000000.0 |
| international_reputation | 14881.0 | 1.119145e+00 | 3.972144e-01 | 1.0 | 1.00 | 1.0 | 1.00 | 5.0 |
| weak_foot | 14881.0 | 2.942477e+00 | 6.580131e-01 | 1.0 | 3.00 | 3.0 | 3.00 | 5.0 |
| skill_moves | 14881.0 | 2.290572e+00 | 7.293560e-01 | 1.0 | 2.00 | 2.0 | 3.00 | 5.0 |
| team_jersey_number | 14710.0 | 1.942536e+01 | 1.619540e+01 | 1.0 | 8.00 | 17.0 | 26.00 | 99.0 |
| contract_valid_until | 14710.0 | 2.017491e+03 | 1.734312e+00 | 2015.0 | 2016.00 | 2017.0 | 2019.00 | 2021.0 |
| pace | 13263.0 | 6.843550e+01 | 1.102372e+01 | 21.0 | 62.00 | 69.0 | 76.00 | 96.0 |
| shooting | 13263.0 | 5.260288e+01 | 1.396492e+01 | 14.0 | 42.00 | 55.0 | 63.00 | 93.0 |
| passing | 13263.0 | 5.707110e+01 | 1.083942e+01 | 20.0 | 50.00 | 58.0 | 65.00 | 93.0 |
| dribbling | 13263.0 | 6.194745e+01 | 1.075474e+01 | 22.0 | 56.00 | 63.0 | 69.00 | 95.0 |
| defending | 13263.0 | 5.099578e+01 | 1.767604e+01 | 15.0 | 34.00 | 56.0 | 65.00 | 90.0 |
| physic | 13263.0 | 6.530287e+01 | 9.631893e+00 | 27.0 | 59.00 | 66.0 | 72.00 | 89.0 |
| gk_diving | 1618.0 | 6.635600e+01 | 7.691925e+00 | 36.0 | 61.00 | 66.0 | 71.00 | 88.0 |
| gk_handling | 1618.0 | 6.244438e+01 | 8.300535e+00 | 42.0 | 56.25 | 63.0 | 68.00 | 87.0 |
| gk_kicking | 1618.0 | 6.101978e+01 | 8.286959e+00 | 29.0 | 55.00 | 60.0 | 66.75 | 91.0 |
| gk_reflexes | 1618.0 | 6.768232e+01 | 8.193498e+00 | 32.0 | 62.00 | 67.0 | 73.00 | 90.0 |
| gk_speed | 1618.0 | 4.375464e+01 | 8.375229e+00 | 12.0 | 39.00 | 44.0 | 48.00 | 64.0 |
| gk_positioning | 1618.0 | 6.318665e+01 | 8.869550e+00 | 37.0 | 57.00 | 63.0 | 69.00 | 90.0 |
These are the statistics for the numerical features. One of them is considered a numeral value but in fact its meaning is categorical - team_jersey_number. This should be taken into account later, in the preparation for submitting the dataset to the ML algorithm. Besides, there are several attributes which are in fact numerical values but due to the way they are input in the table, they are considered non numerical. These are the following columns: 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'defending_marking', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs', 'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm', 'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb'.
The values are written in the format "Number1+number2" (i.e. "78+1", "84+3"). Sample is shown below.
players_16[['attacking_crossing','attacking_finishing', 'attacking_heading_accuracy',
'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
'movement_agility', 'movement_reactions', 'movement_balance',
'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
'mentality_positioning', 'mentality_vision', 'mentality_penalties',
'defending_marking', 'defending_standing_tackle',
'defending_sliding_tackle', 'goalkeeping_diving',
'goalkeeping_handling', 'goalkeeping_kicking',
'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
'rcb', 'rb']].sample(7)
| attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 200197 | 49+1 | 74+1 | 74+1 | 67+1 | 75+1 | 70+1 | 55+1 | 54+1 | 63+1 | 73+1 | 66 | 64 | 67 | 73+1 | 63 | 71+1 | 73+4 | 70 | 67 | 71+1 | 47+1 | 41+1 | 76+1 | 43+1 | 72+1 | 33+1 | 30+1 | 27+1 | 13+1 | 10+1 | 15+1 | 15+1 | 15+1 | 72+0 | 72+0 | 72+0 | 67+0 | 69+0 | 69+0 | 69+0 | 67+0 | 67+0 | 67+0 | 67+0 | 66+0 | 63+0 | 63+0 | 63+0 | 66+0 | 53+0 | 53+0 | 53+0 | 53+0 | 53+0 | 51+0 | 49+0 | 49+0 | 49+0 | 51+0 |
| 158663 | 66+2 | 66-3 | 53+1 | 66-3 | 68+1 | 69-1 | 56+1 | 61+1 | 65+1 | 68-3 | 85 | 84 | 71+2 | 66-1 | 73+1 | 67+1 | 66 | 72 | 65-2 | 67-1 | 61+1 | 52-8 | 62 | 58 | 69+1 | 62 | 61-3 | 59 | 6+1 | 13+1 | 16+1 | 6+1 | 9+1 | 66+0 | 66+0 | 66+0 | 69+0 | 67+0 | 67+0 | 67+0 | 69+0 | 67+0 | 67+0 | 67+0 | 68+0 | 65+0 | 65+0 | 65+0 | 68+0 | 66+0 | 63+0 | 63+0 | 63+0 | 66+0 | 65+0 | 61+0 | 61+0 | 61+0 | 65+0 |
| 201417 | 64+1 | 45+1 | 61+1 | 65+1 | 29+1 | 65+1 | 47+1 | 38+1 | 64+1 | 66+1 | 68-3 | 72 | 62 | 64+1 | 64 | 53+1 | 80 | 70 | 74+1 | 35+1 | 70+1 | 64 | 52+1 | 58+1 | 48+1 | 67+4 | 69+3 | 69+4 | 15+1 | 8+1 | 12+1 | 11+1 | 9+1 | 57+0 | 57+0 | 57+0 | 61+0 | 59+0 | 59+0 | 59+0 | 61+0 | 60+0 | 60+0 | 60+0 | 63+0 | 62+0 | 62+0 | 62+0 | 63+0 | 67+0 | 66+0 | 66+0 | 66+0 | 67+0 | 67+0 | 68+0 | 68+0 | 68+0 | 67+0 |
| 205198 | 67+5 | 53+21 | 54+1 | 63+3 | 32+1 | 67+3 | 55+21 | 41+1 | 61+1 | 54+9 | 67-1 | 66-1 | 74+20 | 47-16 | 76 | 34+1 | 70+15 | 58 | 47 | 52+20 | 63+1 | 55+11 | 56+2 | 45+1 | 42+1 | 52+1 | 51+4 | 46+1 | 12+1 | 9+1 | 15+1 | 10+1 | 10+1 | 53+0 | 53+0 | 53+0 | 59+0 | 56+0 | 56+0 | 56+0 | 59+0 | 57+0 | 57+0 | 57+0 | 59+0 | 56+0 | 56+0 | 56+0 | 59+0 | 57+0 | 55+0 | 55+0 | 55+0 | 57+0 | 56+0 | 53+0 | 53+0 | 53+0 | 56+0 |
| 213423 | 60-3 | 57+1 | 32+1 | 62-1 | 61-2 | 59+1 | 49+1 | 42+1 | 53+1 | 62+1 | 82+9 | 83+5 | 68 | 49+1 | 71 | 58+1 | 53 | 76+8 | 75+10 | 55+1 | 50+1 | 50+1 | 53+1 | 49+1 | 50+1 | 59+1 | 62+1 | 51+1 | 13+1 | 7+1 | 13+1 | 12+1 | 9+1 | 58+0 | 58+0 | 58+0 | 61+0 | 59+0 | 59+0 | 59+0 | 61+0 | 59+0 | 59+0 | 59+0 | 61+0 | 57+0 | 57+0 | 57+0 | 61+0 | 61+0 | 58+0 | 58+0 | 58+0 | 61+0 | 60+0 | 56+0 | 56+0 | 56+0 | 60+0 |
| 219120 | 14 | 12 | 15 | 32 | 9 | 19 | 12 | 11 | 27 | 22 | 44 | 45 | 35 | 48 | 46 | 20 | 58 | 27 | 60 | 11 | 27 | 16 | 14 | 43 | 13 | 18 | 20 | 15 | 60 | 58 | 56 | 60 | 62 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 207513 | 53+1 | 52+1 | 47+1 | 63+1 | 51+1 | 60+1 | 43+1 | 58+1 | 58+1 | 58+1 | 65 | 68 | 57 | 54+1 | 74+1 | 55+1 | 75+4 | 59 | 49 | 54+1 | 60+1 | 54+1 | 53+1 | 51+1 | 55+1 | 51+1 | 54+1 | 56+1 | 15+1 | 14+1 | 6+1 | 8+1 | 15+1 | 55+0 | 55+0 | 55+0 | 57+0 | 57+0 | 57+0 | 57+0 | 57+0 | 57+0 | 57+0 | 57+0 | 58+0 | 57+0 | 57+0 | 57+0 | 58+0 | 57+0 | 56+0 | 56+0 | 56+0 | 57+0 | 57+0 | 55+0 | 55+0 | 55+0 | 57+0 |
The numbers added after the "+/-" sign come from international reputation of player. So, for analysis purposes they can be added/subtracted.
Before that, we have to handle the NaN values in several features. The ones that depict some abilitites of the players. Main reason for not having value is that either they are qualities of field players in the records for a goal keeper or vice versa. So, it is normal to replace them by zeroes.
One more thing is noticed while checking the data. There is a kind of duplication of data. For example, columns "gk_diving","gk_handling", "gk_kicking","gk_reflexes" and "gk_positioning" look quite similar to 'goalkeeping_diving','goalkeeping_handling', 'goalkeeping_kicking','goalkeeping_reflexes','goalkeeping_positioning'. The difference is that in the first group for the field players any rating about goalkeeping capabilities is missing, while in the second group there are some. We would better keep the second group because sometimes GK qualities are useful for field players.
players_16=players_16.drop(["gk_diving","gk_handling", "gk_kicking","gk_reflexes","gk_positioning"],axis=1)
Thus, the columns which need filling with zeroes for NaN values and then transforming this type of string "Number1+number2" into a mathematical calculation are the following ones, put in the list "cols_for_fill".
cols_for_fill=['pace',
'shooting', 'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
'attacking_crossing',
'attacking_finishing', 'attacking_heading_accuracy',
'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
'movement_agility', 'movement_reactions', 'movement_balance',
'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
'mentality_positioning', 'mentality_vision', 'mentality_penalties',
'defending_marking', 'defending_standing_tackle',
'defending_sliding_tackle', 'goalkeeping_diving',
'goalkeeping_handling', 'goalkeeping_kicking',
'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
'rcb', 'rb']
The columns that need transformation are too many and it will be time consuming to do that manually. That's why we will create a function. It converts the values to string, substitutes "NaN" with "0" and then makes the string a mathematical expression (by using eval()).
def transform_columns_to_number(data_frame,columns_to_be_transformed):
for col in columns_to_be_transformed:
data_frame[col]=data_frame[col].astype(str)
data_frame[col]=data_frame[col].replace("nan","0")
data_frame[col]=pd.Series([eval(item) for item in data_frame[col].values]).values
#return data_frame[col]
transform_columns_to_number(players_16,cols_for_fill)
players_16.head()
| short_name | age | dob | height_cm | weight_kg | nationality | club | overall | potential | value_eur | player_positions | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | body_type | real_face | team_position | team_jersey_number | joined | contract_valid_until | pace | shooting | passing | dribbling | defending | physic | gk_speed | player_traits | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 158023 | L. Messi | 28 | 1987-06-24 | 170 | 72 | Argentina | FC Barcelona | 94 | 95 | 111000000 | RW, CF | Left | 5 | 4 | 4 | Medium/Low | Messi | Yes | RW | 10.0 | 2004-07-01 | 2018.0 | 92.0 | 88.0 | 86.0 | 95.0 | 24.0 | 62.0 | 0.0 | Finesse Shot, Speed Dribbler (CPU AI Only), On... | 76 | 92 | 71 | 87 | 85 | 96 | 89 | 90 | 82 | 96 | 94 | 90 | 90 | 90 | 95 | 80 | 63 | 75 | 58 | 88 | 48 | 22 | 88 | 90 | 74 | 1 | 25 | 22 | 6 | 11 | 15 | 14 | 8 | 90 | 90 | 90 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 93 | 85 | 85 | 85 | 93 | 65 | 60 | 60 | 60 | 65 | 60 | 47 | 47 | 47 | 60 |
| 20801 | Cristiano Ronaldo | 30 | 1985-02-05 | 185 | 80 | Portugal | Real Madrid | 93 | 93 | 85500000 | LW, LM | Right | 5 | 4 | 5 | High/Low | C. Ronaldo | Yes | LM | 7.0 | 2009-07-01 | 2018.0 | 92.0 | 93.0 | 80.0 | 91.0 | 33.0 | 78.0 | 0.0 | Power Free-Kick, Flair, Long Shot Taker (CPU A... | 81 | 95 | 86 | 80 | 87 | 93 | 88 | 75 | 72 | 90 | 91 | 92 | 87 | 94 | 61 | 94 | 94 | 85 | 79 | 93 | 61 | 34 | 95 | 81 | 85 | 22 | 31 | 23 | 7 | 11 | 15 | 14 | 11 | 94 | 94 | 94 | 93 | 94 | 94 | 94 | 93 | 91 | 91 | 91 | 91 | 83 | 83 | 83 | 91 | 67 | 63 | 63 | 63 | 67 | 63 | 55 | 55 | 55 | 63 |
| 9014 | A. Robben | 31 | 1984-01-23 | 180 | 80 | Netherlands | FC Bayern München | 90 | 90 | 56000000 | RM, LM, RW | Left | 5 | 2 | 4 | High/Low | Normal | Yes | SUB | 10.0 | 2009-08-28 | 2017.0 | 92.0 | 86.0 | 82.0 | 92.0 | 32.0 | 64.0 | 0.0 | Diver, Injury Prone, Avoids Using Weaker Foot,... | 80 | 85 | 50 | 84 | 86 | 93 | 87 | 83 | 72 | 88 | 91 | 91 | 89 | 91 | 91 | 86 | 61 | 74 | 65 | 90 | 47 | 39 | 89 | 84 | 80 | 29 | 26 | 26 | 10 | 8 | 11 | 5 | 15 | 87 | 87 | 87 | 92 | 91 | 91 | 91 | 92 | 91 | 91 | 91 | 90 | 83 | 83 | 83 | 90 | 68 | 63 | 63 | 63 | 68 | 62 | 50 | 50 | 50 | 62 |
| 167495 | M. Neuer | 29 | 1986-03-27 | 193 | 92 | Germany | FC Bayern München | 90 | 90 | 58000000 | GK | Right | 5 | 4 | 1 | Medium/Medium | Normal | Yes | GK | 1.0 | 2011-07-01 | 2019.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.0 | GK Long Throw, 1-on-1 Rush | 5 | 1 | 25 | 54 | -3 | 7 | 3 | -3 | 53 | 31 | 58 | 61 | 43 | 88 | 35 | 8 | 78 | 44 | 83 | 7 | 29 | 30 | -1 | 90 | 37 | -5 | -5 | -3 | 82 | 89 | 91 | 90 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 176580 | L. Suárez | 28 | 1987-01-24 | 182 | 85 | Uruguay | FC Barcelona | 90 | 90 | 69000000 | ST | Right | 5 | 4 | 4 | High/Medium | Normal | Yes | ST | 9.0 | 2014-07-11 | 2019.0 | 83.0 | 88.0 | 79.0 | 87.0 | 42.0 | 79.0 | 0.0 | Diver, Beat Offside Trap, Flair, Technical Dri... | 77 | 89 | 79 | 82 | 89 | 86 | 86 | 84 | 64 | 93 | 88 | 77 | 86 | 91 | 60 | 92 | 69 | 86 | 76 | 88 | 78 | 41 | 94 | 84 | 85 | 30 | 45 | 38 | 27 | 25 | 31 | 33 | 37 | 90 | 90 | 90 | 90 | 91 | 91 | 91 | 90 | 89 | 89 | 89 | 88 | 82 | 82 | 82 | 88 | 70 | 68 | 68 | 68 | 70 | 67 | 61 | 61 | 61 | 67 |
Need to check for missing data.
players_16.isnull().sum().sort_values(ascending=False)
player_traits 8119 joined 1225 contract_valid_until 171 team_jersey_number 171 team_position 171 attacking_finishing 0 dribbling 0 defending 0 physic 0 gk_speed 0 attacking_crossing 0 attacking_heading_accuracy 0 shooting 0 attacking_short_passing 0 attacking_volleys 0 skill_dribbling 0 skill_curve 0 skill_fk_accuracy 0 skill_long_passing 0 skill_ball_control 0 movement_acceleration 0 movement_sprint_speed 0 passing 0 rb 0 pace 0 movement_reactions 0 age 0 dob 0 height_cm 0 weight_kg 0 nationality 0 club 0 overall 0 potential 0 value_eur 0 player_positions 0 preferred_foot 0 international_reputation 0 weak_foot 0 skill_moves 0 work_rate 0 body_type 0 real_face 0 movement_agility 0 movement_balance 0 rcb 0 power_shot_power 0 cf 0 rf 0 rw 0 lam 0 cam 0 ram 0 lm 0 lcm 0 cm 0 rcm 0 rm 0 lwb 0 ldm 0 cdm 0 rdm 0 rwb 0 lb 0 lcb 0 cb 0 lf 0 lw 0 rs 0 mentality_penalties 0 power_jumping 0 power_stamina 0 power_strength 0 power_long_shots 0 mentality_aggression 0 mentality_interceptions 0 mentality_positioning 0 mentality_vision 0 defending_marking 0 st 0 defending_standing_tackle 0 defending_sliding_tackle 0 goalkeeping_diving 0 goalkeeping_handling 0 goalkeeping_kicking 0 goalkeeping_positioning 0 goalkeeping_reflexes 0 ls 0 short_name 0 dtype: int64
"player_traits" for now we will leave as it is. Anyway, it will not be submitted to the ML algorithm. "joined","team_position" and "contract_valid_until" seems logical to substitute with the most common value. While "team_jersey_number" we would replace with "0". We again need a function which will do the work for us.
def fill_missing_data(dataframe,columns):
for col in columns:
dataframe[col]=dataframe[col].fillna(dataframe[col].mode()[0])
columns=["joined","team_position","contract_valid_until"]
fill_missing_data(players_16,columns)
players_16["team_jersey_number"]=players_16["team_jersey_number"].fillna(0)
players_16.isnull().sum().sort_values(ascending=False)
player_traits 8119 rb 0 movement_reactions 0 shooting 0 passing 0 dribbling 0 defending 0 physic 0 gk_speed 0 attacking_crossing 0 attacking_finishing 0 attacking_heading_accuracy 0 attacking_short_passing 0 attacking_volleys 0 skill_dribbling 0 skill_curve 0 skill_fk_accuracy 0 skill_long_passing 0 skill_ball_control 0 movement_acceleration 0 movement_sprint_speed 0 pace 0 contract_valid_until 0 joined 0 value_eur 0 age 0 dob 0 height_cm 0 weight_kg 0 nationality 0 club 0 overall 0 potential 0 player_positions 0 team_jersey_number 0 preferred_foot 0 international_reputation 0 weak_foot 0 skill_moves 0 work_rate 0 body_type 0 real_face 0 team_position 0 movement_agility 0 movement_balance 0 rcb 0 power_shot_power 0 cf 0 rf 0 rw 0 lam 0 cam 0 ram 0 lm 0 lcm 0 cm 0 rcm 0 rm 0 lwb 0 ldm 0 cdm 0 rdm 0 rwb 0 lb 0 lcb 0 cb 0 lf 0 lw 0 rs 0 mentality_penalties 0 power_jumping 0 power_stamina 0 power_strength 0 power_long_shots 0 mentality_aggression 0 mentality_interceptions 0 mentality_positioning 0 mentality_vision 0 defending_marking 0 st 0 defending_standing_tackle 0 defending_sliding_tackle 0 goalkeeping_diving 0 goalkeeping_handling 0 goalkeeping_kicking 0 goalkeeping_positioning 0 goalkeeping_reflexes 0 ls 0 short_name 0 dtype: int64
Now all columns are filled with values, except for "player_traites".
The aim is to see some interesting statistics about players and teams related to nationality, clubs, prices, age, etc.
First we will group the players by nationality and see which are the top 50 nationalities with largest number of players.
There will be many plottings, so in order to make our lives easy, we'd better make a function with appropriate parameters.
def draw_graphs(dataset,title,analyzed_feature,groupby_feature=None, stat_function=None,asc=None,orient=None,
xtitle=None,ytitle=None,num_recs=None,marker_color="blue"):
"""
Parameters and values they can take:
stat_function="max","sum,"mean","count"
orient="v","h"
"""
if stat_function=="count":
graph_data=dataset.groupby(groupby_feature)[analyzed_feature].count().sort_values(ascending=asc)[0:num_recs]
elif stat_function=="max":
graph_data=dataset.groupby(groupby_feature)[analyzed_feature].max().sort_values(ascending=asc)[0:num_recs]
elif stat_function=="sum":
graph_data=dataset.groupby(groupby_feature)[analyzed_feature].sum().sort_values(ascending=asc)[0:num_recs]
else:
graph_data=dataset.groupby(groupby_feature)[analyzed_feature].mean().sort_values(ascending=asc)[0:num_recs]
if orient=='v':
fig=px.bar(graph_data,x=graph_data.index,y=graph_data,orientation=orient,width=900, height=500)
else:
fig=px.bar(graph_data,x=graph_data,y=graph_data.index,orientation=orient,width=900, height=500)
fig.update_layout(
title_text=title,
xaxis_title_text=xtitle,
yaxis_title_text=ytitle)
fig.update_traces(marker_color=marker_color)
fig.show()
draw_graphs(dataset=players_16,title="Number of players per Nation (top 50)",
analyzed_feature="nationality",groupby_feature="nationality",
asc=False,xtitle="Nation",ytitle="Total number of players",
stat_function="count",
orient='v',num_recs=50,marker_color="violet")
Let's explore the age of players. We will take the top 20 most represented countries. We will calculate the average age per country and see some distributions.
top_20_nations=players_16.groupby("nationality")["nationality"].count().sort_values(ascending=False)[:20]
# Selecting all players which nationalities are in the top 20 most represented countries.
players_top_20_nations=players_16[players_16["nationality"].isin(top_20_nations.index)]
draw_graphs(dataset=players_top_20_nations,title="Average age of player per nationality",
analyzed_feature="age",groupby_feature="nationality",
asc=True,xtitle="Age",ytitle="Nationality",
stat_function="mean",
orient='h',num_recs=20)
The difference between lowest and highest age is about 2.5 years. Which is kind of expected. It is worth mentioning that youngest players come from England and the Netherlands which might be a sign of productive schools for young footballers.
nationality_and_age=players_top_20_nations[["nationality","age"]]
fig = px.box(nationality_and_age, x="nationality", y="age")
fig.show()
It is interesting to see who the outliers are.
nations_list=["England","Sweden", "Colombia","Mexico","Norway"]
outliers=pd.DataFrame()
for nation in nations_list:
player_nationality=nation
nationality_max_age=players_top_20_nations[players_top_20_nations["nationality"]==nation]["age"].max()
outliers=outliers.append(players_top_20_nations[(players_top_20_nations["nationality"]==nation) & (players_top_20_nations["age"]==nationality_max_age)])# & players_top_20_nations["age"]==nationality_max_age])
outliers
| short_name | age | dob | height_cm | weight_kg | nationality | club | overall | potential | value_eur | player_positions | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | body_type | real_face | team_position | team_jersey_number | joined | contract_valid_until | pace | shooting | passing | dribbling | defending | physic | gk_speed | player_traits | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 11728 | B. Richardson | 45 | 1969-08-05 | 185 | 77 | England | Wycombe Wanderers | 44 | 44 | 10000 | GK | Right | 1 | 2 | 1 | Medium/Medium | Stocky | No | SUB | 13.0 | 2014-01-30 | 2021.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 25.0 | NaN | -3 | -3 | -1 | -1 | -1 | -3 | -1 | -3 | 1 | 23 | 25 | 25 | 38 | 34 | 44 | 1 | 51 | 32 | 47 | 7 | 45 | 7 | 1 | 9 | 5 | 3 | -1 | 1 | 37 | 55 | 37 | 59 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 645 | D. Andersson | 42 | 1972-12-18 | 187 | 85 | Sweden | Helsingborgs IF | 57 | 57 | 20000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Normal | No | SUB | 39.0 | 2011-01-01 | 2020.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0 | NaN | -1 | 1 | -1 | 37 | -5 | 1 | 1 | 9 | 32 | 25 | -1 | -1 | -3 | 61 | 25 | 25 | 11 | 1 | 64 | 3 | 34 | 22 | -1 | 31 | -1 | -1 | 11 | 13 | 59 | 52 | 50 | 59 | 63 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 165854 | J. Henao | 43 | 1971-12-30 | 181 | 79 | Colombia | Once Caldas | 65 | 65 | 90000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Stocky | No | SUB | 1.0 | 2010-07-01 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 53.0 | Team Player | 3 | -5 | 5 | 34 | 5 | 13 | 3 | 24 | 48 | 25 | 52 | 53 | 59 | 60 | 57 | 1 | 72 | 34 | 60 | 3 | 32 | 22 | 1 | 27 | 11 | 3 | 1 | -1 | 72 | 70 | 58 | 67 | 62 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 140029 | O. Pérez | 42 | 1973-02-01 | 172 | 75 | Mexico | Pachuca | 70 | 70 | 230000 | GK | Right | 2 | 3 | 1 | Medium/Medium | Stocky | No | GK | 21.0 | 2015-07-01 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 58.0 | GK Up for Corners, GK Long Throw | 13 | 3 | 15 | 27 | 1 | -1 | -1 | 15 | 28 | 20 | 71 | 66 | 69 | 73 | 69 | 18 | 85 | 0 | 66 | 11 | 10 | -1 | -1 | 55 | 24 | -3 | -1 | -3 | 71 | 65 | 66 | 73 | 74 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 31834 | F. Johnsen | 41 | 1974-03-17 | 187 | 79 | Norway | Odds BK | 66 | 66 | 0 | ST | Right | 1 | 4 | 3 | Medium/Medium | Lean | No | SUB | 11.0 | 2010-01-01 | 2015.0 | 39.0 | 70.0 | 52.0 | 56.0 | 41.0 | 62.0 | 0.0 | Power Header | 54 | 72 | 84 | 53 | 73 | 51 | 62 | 52 | 44 | 61 | 15 | 22 | 62 | 53 | 57 | 75 | 70 | 48 | 74 | 66 | 49 | 33 | 77 | 55 | 61 | 33 | 44 | 43 | 15 | 13 | 10 | 9 | 15 | 66 | 66 | 66 | 56 | 60 | 60 | 60 | 56 | 57 | 57 | 57 | 54 | 53 | 53 | 53 | 54 | 45 | 46 | 46 | 46 | 45 | 45 | 50 | 50 | 50 | 45 |
Nothing interesting here since these are players in maybe some very low division clubs.
Which leads us to the idea of having these statistics made per nationality of the club players are in. Let's take the 5 best football leagues in Europe, those of: England, Spain, Italy, Germany and France. In this dataset we don't have information which league each club belongs to. So we have to get this data from another source.
The clubs dataset is created manually by copy/pasting lists with teams which participated in the number one league of each country (above mentioned five) during the 2015/16 season. These lists are taken from Wikipedia.
clubs_data=pd.read_csv("data/Main_leagues_2016.csv",encoding="utf-8",sep="\\t")
C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
clubs_data
| Club | Club_country | League | |
|---|---|---|---|
| 0 | Bournemouth | England | Premier League |
| 1 | Arsenal | England | Premier League |
| 2 | Aston Villa | England | Premier League |
| 3 | Chelsea | England | Premier League |
| 4 | Crystal Palace | England | Premier League |
| 5 | Everton | England | Premier League |
| 6 | Leicester City | England | Premier League |
| 7 | Liverpool | England | Premier League |
| 8 | Manchester City | England | Premier League |
| 9 | Manchester United | England | Premier League |
| 10 | Newcastle United | England | Premier League |
| 11 | Norwich City | England | Premier League |
| 12 | Southampton | England | Premier League |
| 13 | Stoke City | England | Premier League |
| 14 | Sunderland | England | Premier League |
| 15 | Swansea City | England | Premier League |
| 16 | Tottenham Hotspur | England | Premier League |
| 17 | Watford | England | Premier League |
| 18 | West Bromwich Albion | England | Premier League |
| 19 | West Ham United | England | Premier League |
| 20 | Atalanta | Italy | Serie A |
| 21 | Bologna | Italy | Serie A |
| 22 | Carpi | Italy | Serie A |
| 23 | Chievo Verona | Italy | Serie A |
| 24 | Empoli | Italy | Serie A |
| 25 | Fiorentina | Italy | Serie A |
| 26 | Frosinone | Italy | Serie A |
| 27 | Genoa | Italy | Serie A |
| 28 | Hellas Verona | Italy | Serie A |
| 29 | Inter | Italy | Serie A |
| 30 | Juventus | Italy | Serie A |
| 31 | Lazio | Italy | Serie A |
| 32 | Milan | Italy | Serie A |
| 33 | Napoli | Italy | Serie A |
| 34 | Palermo | Italy | Serie A |
| 35 | Roma | Italy | Serie A |
| 36 | Sampdoria | Italy | Serie A |
| 37 | Sassuolo | Italy | Serie A |
| 38 | Torino | Italy | Serie A |
| 39 | Udinese | Italy | Serie A |
| 40 | FC Augsburg | Germany | Bundesliga |
| 41 | Bayer 04 Leverkusen | Germany | Bundesliga |
| 42 | FC Bayern München | Germany | Bundesliga |
| 43 | Borussia Dortmund | Germany | Bundesliga |
| 44 | Borussia Mönchengladbach | Germany | Bundesliga |
| 45 | SV Darmstadt 98 | Germany | Bundesliga |
| 46 | Eintracht Frankfurt | Germany | Bundesliga |
| 47 | Hamburger SV | Germany | Bundesliga |
| 48 | Hannover 96 | Germany | Bundesliga |
| 49 | Hertha BSC | Germany | Bundesliga |
| 50 | TSG 1899 Hoffenheim | Germany | Bundesliga |
| 51 | FC Ingolstadt 04 | Germany | Bundesliga |
| 52 | 1. FC Köln | Germany | Bundesliga |
| 53 | 1. FSV Mainz 05 | Germany | Bundesliga |
| 54 | FC Schalke 04 | Germany | Bundesliga |
| 55 | VfB Stuttgart | Germany | Bundesliga |
| 56 | SV Werder Bremen | Germany | Bundesliga |
| 57 | VfL Wolfsburg | Germany | Bundesliga |
| 58 | Bilbao Athletic | Spain | La Liga |
| 59 | Atlético Madrid | Spain | La Liga |
| 60 | FC Barcelona | Spain | La Liga |
| 61 | RC Celta | Spain | La Liga |
| 62 | Deportivo de La Coruña | Spain | La Liga |
| 63 | SD Eibar | Spain | La Liga |
| 64 | RCD Espanyol | Spain | La Liga |
| 65 | Getafe CF | Spain | La Liga |
| 66 | Granada CF | Spain | La Liga |
| 67 | UD Las Palmas | Spain | La Liga |
| 68 | Levante UD | Spain | La Liga |
| 69 | Málaga | Spain | La Liga |
| 70 | Rayo Vallecano | Spain | La Liga |
| 71 | Real Betis | Spain | La Liga |
| 72 | Real Madrid | Spain | La Liga |
| 73 | Real Sociedad | Spain | La Liga |
| 74 | Sevilla FC | Spain | La Liga |
| 75 | Real Sporting de Gijón | Spain | La Liga |
| 76 | Valencia CF | Spain | La Liga |
| 77 | Villarreal CF | Spain | La Liga |
| 78 | Angers SCO | France | Ligue 1 |
| 79 | Sporting Club de Bastia | France | Ligue 1 |
| 80 | FC Girondins de Bordeaux | France | Ligue 1 |
| 81 | Stade Malherbe Caen | France | Ligue 1 |
| 82 | GFC Ajaccio | France | Ligue 1 |
| 83 | En Avant de Guingamp | France | Ligue 1 |
| 84 | LOSC Lille | France | Ligue 1 |
| 85 | FC Lorient | France | Ligue 1 |
| 86 | Olympique Lyonnais | France | Ligue 1 |
| 87 | Olympique de Marseille | France | Ligue 1 |
| 88 | AS Monaco | France | Ligue 1 |
| 89 | Montpellier HSC | France | Ligue 1 |
| 90 | FC Nantes | France | Ligue 1 |
| 91 | OGC Nice | France | Ligue 1 |
| 92 | Paris Saint-Germain | France | Ligue 1 |
| 93 | Stade de Reims | France | Ligue 1 |
| 94 | Stade Rennais FC | France | Ligue 1 |
| 95 | AS Saint-Étienne | France | Ligue 1 |
| 96 | Toulouse Football Club | France | Ligue 1 |
| 97 | ESTAC Troyes | France | Ligue 1 |
#Count the number of teams in each league
clubs_data.groupby("League")["League"].count()
League Bundesliga 18 La Liga 20 Ligue 1 20 Premier League 20 Serie A 20 Name: League, dtype: int64
Data seems ok. But we have to check if the names of clubs coinside in both tables (most probably, not exactly). In list "missing" we will put those club names from clubs_data which are not found in players_16.
missing=[club for club in clubs_data["Club"].unique() if club not in (players_16["club"].unique())]
missing
['Málaga']
This is quite strange that "Malaga" is missing in the data base. The manual search also didn't give any result. We have to live with that. One thing which was nothiced is that one and the same club has two ways of naming: "Athletic Club de Bilbao" and "Bilbao Athletic". In general, this club is known as "Athletico Bilbao", so I will rename all occurrences.
players_16["club"]=players_16["club"].str.replace("Athletic Club de Bilbao","Athletico Bilbao")
players_16["club"]=players_16["club"].str.replace("Bilbao Athletic" ,"Athletico Bilbao")
Here we will check if the substitution went correctly. All clubs containing "Bilbao" should be only "Athletic Bilbao".
players_16[(players_16["club"].str.find("Bilbao"))!=-1]["club"].unique()
array(['Athletico Bilbao'], dtype=object)
Finally, we can merge the datasets and sametime make s sub dataset with players only from the major European leagues and see statistics for them.
euro_league_players=players_16.merge(clubs_data, left_on='club', right_on='Club')
euro_league_players.shape
(2714, 92)
euro_league_players.head()
| short_name | age | dob | height_cm | weight_kg | nationality | club | overall | potential | value_eur | player_positions | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | body_type | real_face | team_position | team_jersey_number | joined | contract_valid_until | pace | shooting | passing | dribbling | defending | physic | gk_speed | player_traits | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | Club | Club_country | League | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | L. Messi | 28 | 1987-06-24 | 170 | 72 | Argentina | FC Barcelona | 94 | 95 | 111000000 | RW, CF | Left | 5 | 4 | 4 | Medium/Low | Messi | Yes | RW | 10.0 | 2004-07-01 | 2018.0 | 92.0 | 88.0 | 86.0 | 95.0 | 24.0 | 62.0 | 0.0 | Finesse Shot, Speed Dribbler (CPU AI Only), On... | 76 | 92 | 71 | 87 | 85 | 96 | 89 | 90 | 82 | 96 | 94 | 90 | 90 | 90 | 95 | 80 | 63 | 75 | 58 | 88 | 48 | 22 | 88 | 90 | 74 | 1 | 25 | 22 | 6 | 11 | 15 | 14 | 8 | 90 | 90 | 90 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 93 | 85 | 85 | 85 | 93 | 65 | 60 | 60 | 60 | 65 | 60 | 47 | 47 | 47 | 60 | FC Barcelona | Spain | La Liga |
| 1 | L. Suárez | 28 | 1987-01-24 | 182 | 85 | Uruguay | FC Barcelona | 90 | 90 | 69000000 | ST | Right | 5 | 4 | 4 | High/Medium | Normal | Yes | ST | 9.0 | 2014-07-11 | 2019.0 | 83.0 | 88.0 | 79.0 | 87.0 | 42.0 | 79.0 | 0.0 | Diver, Beat Offside Trap, Flair, Technical Dri... | 77 | 89 | 79 | 82 | 89 | 86 | 86 | 84 | 64 | 93 | 88 | 77 | 86 | 91 | 60 | 92 | 69 | 86 | 76 | 88 | 78 | 41 | 94 | 84 | 85 | 30 | 45 | 38 | 27 | 25 | 31 | 33 | 37 | 90 | 90 | 90 | 90 | 91 | 91 | 91 | 90 | 89 | 89 | 89 | 88 | 82 | 82 | 82 | 88 | 70 | 68 | 68 | 68 | 70 | 67 | 61 | 61 | 61 | 67 | FC Barcelona | Spain | La Liga |
| 2 | Neymar | 23 | 1992-02-05 | 174 | 68 | Brazil | FC Barcelona | 88 | 93 | 71500000 | LW | Right | 5 | 5 | 5 | High/Medium | Neymar | Yes | LW | 11.0 | 2013-07-01 | 2018.0 | 90.0 | 80.0 | 72.0 | 92.0 | 30.0 | 57.0 | 0.0 | Diver, Flair, Technical Dribbler (CPU AI Only) | 71 | 85 | 62 | 72 | 83 | 94 | 78 | 78 | 72 | 92 | 91 | 91 | 92 | 86 | 84 | 77 | 61 | 74 | 45 | 70 | 56 | 36 | 87 | 72 | 81 | 21 | 24 | 33 | 9 | 9 | 15 | 15 | 11 | 84 | 84 | 84 | 88 | 87 | 87 | 87 | 88 | 86 | 86 | 86 | 86 | 78 | 78 | 78 | 86 | 65 | 59 | 59 | 59 | 65 | 61 | 48 | 48 | 48 | 61 | FC Barcelona | Spain | La Liga |
| 3 | Sergio Busquets | 26 | 1988-07-16 | 189 | 76 | Spain | FC Barcelona | 86 | 88 | 39000000 | CDM | Right | 4 | 3 | 3 | Medium/Medium | Lean | Yes | CDM | 5.0 | 2008-09-01 | 2019.0 | 53.0 | 59.0 | 78.0 | 75.0 | 83.0 | 81.0 | 0.0 | Diver | 62 | 60 | 70 | 87 | 44 | 74 | 66 | 68 | 80 | 83 | 53 | 48 | 57 | 82 | 55 | 61 | 66 | 86 | 77 | 54 | 86 | 86 | 77 | 85 | 60 | 88 | 88 | 81 | 5 | 8 | 13 | 9 | 13 | 71 | 71 | 71 | 73 | 75 | 75 | 75 | 73 | 78 | 78 | 78 | 76 | 83 | 83 | 83 | 76 | 80 | 86 | 86 | 86 | 80 | 80 | 83 | 83 | 83 | 80 | FC Barcelona | Spain | La Liga |
| 4 | Piqué | 28 | 1987-02-02 | 193 | 85 | Spain | FC Barcelona | 85 | 86 | 29500000 | CB | Right | 4 | 3 | 2 | High/Medium | Normal | Yes | RCB | 3.0 | 2008-07-01 | 2019.0 | 64.0 | 61.0 | 70.0 | 64.0 | 86.0 | 76.0 | 0.0 | Long Passer (CPU AI Only) | 64 | 70 | 82 | 82 | 57 | 68 | 73 | 43 | 76 | 76 | 56 | 67 | 57 | 81 | 44 | 71 | 74 | 69 | 83 | 60 | 68 | 88 | 66 | 62 | 69 | 86 | 87 | 83 | 10 | 11 | 14 | 15 | 8 | 70 | 70 | 70 | 68 | 69 | 69 | 69 | 68 | 70 | 70 | 70 | 70 | 76 | 76 | 76 | 70 | 78 | 83 | 83 | 83 | 78 | 80 | 85 | 85 | 85 | 80 | FC Barcelona | Spain | La Liga |
draw_graphs(dataset=euro_league_players,title="Average age per league",
analyzed_feature="age",groupby_feature="League",
asc=False,xtitle="League",ytitle="Age",
stat_function="mean",
orient='v',marker_color="lightblue")
It is interesting to see that England was the country with youngest players (average age is: 23.78) but the average age in Premier League is quite high 25.21.
Below we will show boxplots of ages per league.
league_and_age=euro_league_players[["League","age"]]
fig = px.box(league_and_age, x="League", y="age",color="League",title="Age per League")
fig.show()
Let's check again the outliers because it is interesting to see who is playeing at the age of 42 and 40 in such serious tournaments. We will show the oldest footballer in each league.
#leagues_list=["England","Sweden", "Colombia","Mexico","Norway"]
euro_outliers=pd.DataFrame()
for league in euro_league_players["League"].unique():
player_league=league
league_max_age=euro_league_players[euro_league_players["League"]==league]["age"].max()
euro_outliers=euro_outliers.append(euro_league_players[(euro_league_players["League"]==league) & (euro_league_players["age"]==league_max_age)])# & players_top_20_nations["age"]==nationality_max_age])
euro_outliers
| short_name | age | dob | height_cm | weight_kg | nationality | club | overall | potential | value_eur | player_positions | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | body_type | real_face | team_position | team_jersey_number | joined | contract_valid_until | pace | shooting | passing | dribbling | defending | physic | gk_speed | player_traits | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | Club | Club_country | League | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2418 | Valerón | 40 | 1975-06-17 | 186 | 76 | Spain | UD Las Palmas | 74 | 74 | 0 | CAM, CM | Right | 2 | 4 | 4 | Medium/Low | Lean | Yes | SUB | 21.0 | 2013-07-15 | 2016.0 | 34.0 | 59.0 | 80.0 | 76.0 | 23.0 | 36.0 | 0.0 | Injury Free, Playmaker (CPU AI Only), Swerve P... | 73 | 61 | 64 | 89 | 60 | 80 | 73 | 68 | 74 | 85 | 30 | 32 | 49 | 82 | 65 | 50 | 27 | 19 | 43 | 64 | 35 | 28 | 65 | 89 | 67 | 3 | 15 | 3 | 8 | 9 | 15 | 10 | 9 | 63 | 63 | 63 | 69 | 70 | 70 | 70 | 69 | 74 | 74 | 74 | 69 | 70 | 70 | 70 | 69 | 47 | 50 | 50 | 50 | 47 | 42 | 36 | 36 | 36 | 42 | UD Las Palmas | Spain | La Liga |
| 1774 | A. Manninger | 38 | 1977-06-04 | 189 | 85 | Austria | FC Augsburg | 72 | 72 | 350000 | GK | Right | 2 | 2 | 1 | Medium/Medium | Normal | Yes | SUB | 1.0 | 2012-11-21 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 40.0 | Puncher, Team Player | -1 | -1 | 1 | 23 | 3 | 1 | 5 | 5 | 9 | 0 | 39 | 40 | 48 | 77 | 45 | 23 | 70 | 31 | 74 | -3 | 40 | 7 | -7 | 3 | 9 | -3 | 3 | 5 | 73 | 73 | 65 | 73 | 75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | FC Augsburg | Germany | Bundesliga |
| 1394 | M. Schwarzer | 42 | 1972-10-06 | 194 | 95 | Australia | Leicester City | 73 | 73 | 500000 | GK | Right | 2 | 3 | 1 | Medium/Medium | Normal | No | SUB | 32.0 | 2015-01-06 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 48.0 | NaN | 1 | -3 | 3 | 25 | 9 | 1 | 3 | -3 | 24 | 9 | 50 | 46 | 36 | 76 | 43 | 30 | 57 | -7 | 70 | 11 | 41 | 23 | 13 | 42 | 28 | 9 | 13 | 15 | 69 | 73 | 68 | 79 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Leicester City | England | Premier League |
| 2603 | B. Nivet | 38 | 1977-01-02 | 178 | 75 | France | ESTAC Troyes | 73 | 73 | 300000 | CAM, CM | Right | 2 | 3 | 3 | Medium/Medium | Normal | No | SUB | 10.0 | 2012-06-12 | 2016.0 | 59.0 | 70.0 | 74.0 | 72.0 | 55.0 | 61.0 | 0.0 | Playmaker (CPU AI Only), Team Player | 63 | 73 | 59 | 79 | 65 | 75 | 74 | 74 | 74 | 80 | 54 | 60 | 64 | 71 | 72 | 72 | 24 | 59 | 70 | 68 | 59 | 55 | 70 | 82 | 77 | 54 | 60 | 46 | 12 | 8 | 9 | 15 | 8 | 69 | 69 | 69 | 70 | 71 | 71 | 71 | 70 | 73 | 73 | 73 | 70 | 72 | 72 | 72 | 70 | 61 | 65 | 65 | 65 | 61 | 60 | 58 | 58 | 58 | 60 | ESTAC Troyes | France | Ligue 1 |
| 857 | L. Castellazzi | 39 | 1975-07-19 | 192 | 89 | Italy | Torino | 72 | 72 | 350000 | GK | Left | 2 | 2 | 1 | Medium/Medium | Lean | No | RES | 13.0 | 2014-09-01 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 33.0 | Puncher | -1 | -3 | -3 | 32 | -3 | 7 | 13 | 1 | 28 | 23 | 9 | 13 | 53 | 66 | 55 | 5 | 59 | 31 | 76 | 5 | 30 | 23 | 3 | 45 | 6 | 1 | -1 | 7 | 76 | 76 | 66 | 84 | 66 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Torino | Italy | Serie A |
And now let's finally see the money part. First we will check the major leagues, after that will make the analysis on the whole dataset.
euro_leagues_value=euro_league_players.groupby("League")["value_eur"].sum().sort_values()
draw_graphs(dataset=euro_league_players,title="Total value per league",
analyzed_feature="value_eur",groupby_feature="League",
asc=True,xtitle="Total Value",ytitle="League",
stat_function="sum",
orient='h',marker_color="lightgreen")
draw_graphs(dataset=euro_league_players,title="Average player value per league",
analyzed_feature="value_eur",groupby_feature="League",
asc=True,xtitle="Average Value",ytitle="Age",
stat_function="mean",
orient='h',marker_color="violet")
draw_graphs(dataset=euro_league_players,title="Total value per club",
analyzed_feature="value_eur",groupby_feature="club",
asc=False,xtitle="Club",ytitle="Total Value",
stat_function="sum",
orient='v',num_recs=20,marker_color="lightyellow")
draw_graphs(dataset=euro_league_players,title="Average value per club",
analyzed_feature="value_eur",groupby_feature="club",
asc=False,xtitle="Club",ytitle="Average Value",
stat_function="mean",
orient='v',num_recs=20,marker_color="orange")
draw_graphs(dataset=euro_league_players,title="Maximum player value per club",
analyzed_feature="value_eur",groupby_feature="club",
asc=False,xtitle="Club",ytitle="Maximum Value",
stat_function="max",
orient='v',num_recs=20,marker_color="rosybrown")
players_top_20_nations
| short_name | age | dob | height_cm | weight_kg | nationality | club | overall | potential | value_eur | player_positions | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | body_type | real_face | team_position | team_jersey_number | joined | contract_valid_until | pace | shooting | passing | dribbling | defending | physic | gk_speed | player_traits | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 158023 | L. Messi | 28 | 1987-06-24 | 170 | 72 | Argentina | FC Barcelona | 94 | 95 | 111000000 | RW, CF | Left | 5 | 4 | 4 | Medium/Low | Messi | Yes | RW | 10.0 | 2004-07-01 | 2018.0 | 92.0 | 88.0 | 86.0 | 95.0 | 24.0 | 62.0 | 0.0 | Finesse Shot, Speed Dribbler (CPU AI Only), On... | 76 | 92 | 71 | 87 | 85 | 96 | 89 | 90 | 82 | 96 | 94 | 90 | 90 | 90 | 95 | 80 | 63 | 75 | 58 | 88 | 48 | 22 | 88 | 90 | 74 | 1 | 25 | 22 | 6 | 11 | 15 | 14 | 8 | 90 | 90 | 90 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 93 | 85 | 85 | 85 | 93 | 65 | 60 | 60 | 60 | 65 | 60 | 47 | 47 | 47 | 60 |
| 20801 | Cristiano Ronaldo | 30 | 1985-02-05 | 185 | 80 | Portugal | Real Madrid | 93 | 93 | 85500000 | LW, LM | Right | 5 | 4 | 5 | High/Low | C. Ronaldo | Yes | LM | 7.0 | 2009-07-01 | 2018.0 | 92.0 | 93.0 | 80.0 | 91.0 | 33.0 | 78.0 | 0.0 | Power Free-Kick, Flair, Long Shot Taker (CPU A... | 81 | 95 | 86 | 80 | 87 | 93 | 88 | 75 | 72 | 90 | 91 | 92 | 87 | 94 | 61 | 94 | 94 | 85 | 79 | 93 | 61 | 34 | 95 | 81 | 85 | 22 | 31 | 23 | 7 | 11 | 15 | 14 | 11 | 94 | 94 | 94 | 93 | 94 | 94 | 94 | 93 | 91 | 91 | 91 | 91 | 83 | 83 | 83 | 91 | 67 | 63 | 63 | 63 | 67 | 63 | 55 | 55 | 55 | 63 |
| 9014 | A. Robben | 31 | 1984-01-23 | 180 | 80 | Netherlands | FC Bayern München | 90 | 90 | 56000000 | RM, LM, RW | Left | 5 | 2 | 4 | High/Low | Normal | Yes | SUB | 10.0 | 2009-08-28 | 2017.0 | 92.0 | 86.0 | 82.0 | 92.0 | 32.0 | 64.0 | 0.0 | Diver, Injury Prone, Avoids Using Weaker Foot,... | 80 | 85 | 50 | 84 | 86 | 93 | 87 | 83 | 72 | 88 | 91 | 91 | 89 | 91 | 91 | 86 | 61 | 74 | 65 | 90 | 47 | 39 | 89 | 84 | 80 | 29 | 26 | 26 | 10 | 8 | 11 | 5 | 15 | 87 | 87 | 87 | 92 | 91 | 91 | 91 | 92 | 91 | 91 | 91 | 90 | 83 | 83 | 83 | 90 | 68 | 63 | 63 | 63 | 68 | 62 | 50 | 50 | 50 | 62 |
| 167495 | M. Neuer | 29 | 1986-03-27 | 193 | 92 | Germany | FC Bayern München | 90 | 90 | 58000000 | GK | Right | 5 | 4 | 1 | Medium/Medium | Normal | Yes | GK | 1.0 | 2011-07-01 | 2019.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.0 | GK Long Throw, 1-on-1 Rush | 5 | 1 | 25 | 54 | -3 | 7 | 3 | -3 | 53 | 31 | 58 | 61 | 43 | 88 | 35 | 8 | 78 | 44 | 83 | 7 | 29 | 30 | -1 | 90 | 37 | -5 | -5 | -3 | 82 | 89 | 91 | 90 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 41236 | Z. Ibrahimović | 33 | 1981-10-03 | 195 | 95 | Sweden | Paris Saint-Germain | 89 | 89 | 40500000 | ST | Right | 5 | 4 | 4 | Medium/Low | Normal | Yes | ST | 10.0 | 2012-07-01 | 2016.0 | 73.0 | 90.0 | 81.0 | 85.0 | 31.0 | 86.0 | 0.0 | Power Free-Kick, Leadership, Flair, Long Shot ... | 76 | 90 | 76 | 84 | 92 | 87 | 80 | 80 | 76 | 90 | 70 | 71 | 86 | 85 | 41 | 93 | 72 | 72 | 93 | 88 | 84 | 20 | 86 | 83 | 91 | 5 | 41 | 27 | 13 | 15 | 10 | 9 | 12 | 89 | 89 | 89 | 87 | 89 | 89 | 89 | 87 | 88 | 88 | 88 | 86 | 81 | 81 | 81 | 86 | 63 | 63 | 63 | 63 | 63 | 59 | 56 | 56 | 56 | 59 |
| 190871 | Neymar | 23 | 1992-02-05 | 174 | 68 | Brazil | FC Barcelona | 88 | 93 | 71500000 | LW | Right | 5 | 5 | 5 | High/Medium | Neymar | Yes | LW | 11.0 | 2013-07-01 | 2018.0 | 90.0 | 80.0 | 72.0 | 92.0 | 30.0 | 57.0 | 0.0 | Diver, Flair, Technical Dribbler (CPU AI Only) | 71 | 85 | 62 | 72 | 83 | 94 | 78 | 78 | 72 | 92 | 91 | 91 | 92 | 86 | 84 | 77 | 61 | 74 | 45 | 70 | 56 | 36 | 87 | 72 | 81 | 21 | 24 | 33 | 9 | 9 | 15 | 15 | 11 | 84 | 84 | 84 | 88 | 87 | 87 | 87 | 88 | 86 | 86 | 86 | 86 | 78 | 78 | 78 | 86 | 65 | 59 | 59 | 59 | 65 | 61 | 48 | 48 | 48 | 61 |
| 164240 | Thiago Silva | 30 | 1984-09-22 | 183 | 79 | Brazil | Paris Saint-Germain | 88 | 88 | 38000000 | CB | Right | 4 | 3 | 3 | High/High | Normal | Yes | RCB | 2.0 | 2012-07-01 | 2018.0 | 74.0 | 57.0 | 73.0 | 73.0 | 90.0 | 79.0 | 0.0 | Leadership, Long Passer (CPU AI Only), Power H... | 60 | 38 | 82 | 79 | 63 | 68 | 61 | 73 | 81 | 80 | 69 | 72 | 75 | 85 | 68 | 78 | 96 | 68 | 81 | 71 | 79 | 91 | 59 | 74 | 71 | 92 | 92 | 90 | 9 | 12 | 5 | 9 | 10 | 70 | 70 | 70 | 71 | 72 | 72 | 72 | 71 | 74 | 74 | 74 | 73 | 79 | 79 | 79 | 73 | 82 | 86 | 86 | 86 | 82 | 84 | 88 | 88 | 88 | 84 |
| 168542 | David Silva | 29 | 1986-01-08 | 170 | 67 | Spain | Manchester City | 88 | 88 | 50500000 | CAM, LM | Left | 4 | 2 | 4 | High/Low | Normal | Yes | SUB | 21.0 | 2010-07-14 | 2019.0 | 73.0 | 74.0 | 89.0 | 89.0 | 32.0 | 59.0 | 0.0 | Avoids Using Weaker Foot, Flair, Playmaker (CP... | 91 | 71 | 50 | 96 | 80 | 87 | 83 | 77 | 85 | 91 | 76 | 65 | 93 | 84 | 88 | 71 | 66 | 68 | 59 | 78 | 51 | 41 | 84 | 96 | 77 | 23 | 30 | 29 | 1 | 1 | 1 | 1 | 1 | 78 | 78 | 78 | 86 | 85 | 85 | 85 | 86 | 88 | 88 | 88 | 86 | 84 | 84 | 84 | 86 | 65 | 64 | 64 | 64 | 65 | 60 | 49 | 49 | 49 | 60 |
| 198710 | J. Rodríguez | 23 | 1991-07-12 | 180 | 75 | Colombia | Real Madrid | 87 | 93 | 62500000 | CAM, CM, RM | Left | 4 | 3 | 4 | Medium/Medium | Normal | Yes | CAM | 10.0 | 2014-07-22 | 2020.0 | 78.0 | 86.0 | 85.0 | 85.0 | 43.0 | 72.0 | 0.0 | Flair, Playmaker (CPU AI Only), Technical Drib... | 95 | 90 | 68 | 85 | 100 | 88 | 79 | 89 | 76 | 85 | 77 | 77 | 82 | 85 | 74 | 93 | 55 | 75 | 69 | 91 | 74 | 58 | 86 | 87 | 81 | 25 | 58 | 30 | 15 | 15 | 15 | 5 | 14 | 84 | 84 | 84 | 87 | 86 | 86 | 86 | 87 | 87 | 87 | 87 | 86 | 83 | 83 | 83 | 86 | 69 | 68 | 68 | 68 | 69 | 65 | 58 | 58 | 58 | 65 |
| 182521 | T. Kroos | 25 | 1990-01-04 | 182 | 78 | Germany | Real Madrid | 87 | 90 | 54500000 | CM, CDM | Right | 4 | 5 | 3 | Medium/Medium | Normal | Yes | RDM | 8.0 | 2014-07-17 | 2020.0 | 56.0 | 81.0 | 88.0 | 82.0 | 66.0 | 69.0 | 0.0 | Long Shot Taker (CPU AI Only), Playmaker (CPU ... | 84 | 76 | 54 | 88 | 80 | 84 | 80 | 83 | 89 | 87 | 57 | 50 | 67 | 85 | 46 | 87 | 54 | 78 | 70 | 90 | 54 | 80 | 77 | 88 | 73 | 60 | 76 | 59 | 10 | 11 | 13 | 7 | 10 | 78 | 78 | 78 | 82 | 82 | 82 | 82 | 82 | 85 | 85 | 85 | 82 | 87 | 87 | 87 | 82 | 77 | 79 | 79 | 79 | 77 | 74 | 70 | 70 | 70 | 74 |
| 183907 | J. Boateng | 26 | 1988-09-03 | 192 | 90 | Germany | FC Bayern München | 87 | 89 | 45000000 | CB | Right | 4 | 4 | 2 | Medium/Medium | Normal | Yes | CB | 17.0 | 2011-07-14 | 2018.0 | 79.0 | 50.0 | 69.0 | 68.0 | 87.0 | 84.0 | 0.0 | Long Passer (CPU AI Only), Giant Throw-in | 69 | 34 | 88 | 73 | 48 | 67 | 56 | 36 | 74 | 70 | 74 | 81 | 51 | 82 | 55 | 80 | 74 | 77 | 90 | 58 | 82 | 84 | 47 | 75 | 46 | 84 | 92 | 92 | 7 | 12 | 15 | 6 | 5 | 68 | 68 | 68 | 68 | 68 | 68 | 68 | 68 | 69 | 69 | 69 | 71 | 74 | 74 | 74 | 71 | 82 | 83 | 83 | 83 | 82 | 84 | 87 | 87 | 87 | 84 |
| 188545 | R. Lewandowski | 26 | 1988-08-21 | 185 | 79 | Poland | FC Bayern München | 87 | 89 | 55000000 | ST | Right | 4 | 4 | 3 | Medium/Medium | Normal | Yes | ST | 9.0 | 2014-07-01 | 2019.0 | 80.0 | 85.0 | 74.0 | 84.0 | 38.0 | 80.0 | 0.0 | Injury Free, Chip Shot (CPU AI Only) | 62 | 88 | 83 | 81 | 82 | 84 | 77 | 68 | 64 | 87 | 79 | 81 | 72 | 88 | 75 | 84 | 83 | 83 | 83 | 80 | 80 | 39 | 87 | 77 | 77 | 25 | 42 | 13 | 15 | 6 | 12 | 8 | 10 | 87 | 87 | 87 | 84 | 86 | 86 | 86 | 84 | 85 | 85 | 85 | 83 | 79 | 79 | 79 | 83 | 63 | 64 | 64 | 64 | 63 | 60 | 58 | 58 | 58 | 60 |
| 176635 | M. Özil | 26 | 1988-10-15 | 183 | 76 | Germany | Arsenal | 87 | 88 | 52500000 | CAM, LW | Left | 4 | 2 | 4 | Medium/Low | Lean | Yes | CAM | 11.0 | 2013-09-02 | 2018.0 | 72.0 | 74.0 | 85.0 | 86.0 | 24.0 | 57.0 | 0.0 | Finesse Shot, Flair, Playmaker (CPU AI Only), ... | 80 | 74 | 54 | 87 | 77 | 86 | 84 | 79 | 80 | 90 | 77 | 65 | 84 | 87 | 64 | 70 | 51 | 64 | 57 | 75 | 54 | 24 | 85 | 92 | 76 | 22 | 7 | 13 | 6 | 14 | 10 | 6 | 14 | 78 | 78 | 78 | 85 | 84 | 84 | 84 | 85 | 87 | 87 | 87 | 84 | 80 | 80 | 80 | 84 | 59 | 58 | 58 | 58 | 59 | 54 | 43 | 43 | 43 | 54 |
| 121939 | P. Lahm | 31 | 1983-11-11 | 170 | 66 | Germany | FC Bayern München | 87 | 87 | 29500000 | CDM, RB, CM | Right | 4 | 3 | 3 | Medium/High | Normal | Yes | RM | 21.0 | 2002-11-01 | 2018.0 | 75.0 | 56.0 | 84.0 | 85.0 | 87.0 | 66.0 | 0.0 | Dives Into Tackles (CPU AI Only), Leadership, ... | 84 | 47 | 62 | 88 | 66 | 82 | 77 | 59 | 84 | 87 | 77 | 73 | 82 | 90 | 94 | 57 | 72 | 82 | 59 | 65 | 58 | 94 | 69 | 82 | 71 | 87 | 86 | 95 | 11 | 12 | 5 | 14 | 5 | 71 | 71 | 71 | 80 | 78 | 78 | 78 | 80 | 82 | 82 | 82 | 82 | 86 | 86 | 86 | 82 | 89 | 87 | 87 | 87 | 89 | 88 | 83 | 83 | 83 | 88 |
| 138956 | G. Chiellini | 30 | 1984-08-14 | 186 | 76 | Italy | Juventus | 87 | 87 | 32500000 | CB | Left | 4 | 2 | 2 | Low/High | Normal | Yes | LCB | 3.0 | 2005-07-01 | 2018.0 | 77.0 | 47.0 | 56.0 | 58.0 | 90.0 | 84.0 | 0.0 | Long Throw-in, Avoids Using Weaker Foot, Dives... | 68 | 38 | 88 | 61 | 61 | 56 | 60 | 31 | 71 | 56 | 77 | 79 | 71 | 82 | 78 | 77 | 88 | 63 | 88 | 48 | 91 | 87 | 34 | 55 | 50 | 92 | 92 | 95 | 3 | 3 | 2 | 4 | 3 | 61 | 61 | 61 | 58 | 57 | 57 | 57 | 58 | 56 | 56 | 56 | 60 | 62 | 62 | 62 | 60 | 77 | 77 | 77 | 77 | 77 | 80 | 87 | 87 | 87 | 80 |
| 153079 | S. Agüero | 27 | 1988-06-02 | 172 | 74 | Argentina | Manchester City | 87 | 87 | 47500000 | ST | Right | 4 | 4 | 4 | High/Medium | Normal | Yes | ST | 10.0 | 2011-07-28 | 2019.0 | 89.0 | 87.0 | 77.0 | 89.0 | 23.0 | 68.0 | 0.0 | Injury Prone, Beat Offside Trap, Flair, Techni... | 70 | 90 | 68 | 82 | 85 | 89 | 82 | 72 | 63 | 89 | 92 | 88 | 86 | 88 | 90 | 85 | 76 | 69 | 72 | 82 | 57 | 24 | 90 | 83 | 83 | 1 | 20 | -1 | 13 | 15 | 6 | 11 | 14 | 87 | 87 | 87 | 88 | 89 | 89 | 89 | 88 | 88 | 88 | 88 | 86 | 78 | 78 | 78 | 86 | 59 | 56 | 56 | 56 | 59 | 54 | 45 | 45 | 45 | 54 |
| 155862 | Sergio Ramos | 29 | 1986-03-30 | 183 | 75 | Spain | Real Madrid | 87 | 87 | 34000000 | CB | Right | 4 | 3 | 3 | High/Medium | Normal | Yes | LCB | 4.0 | 2005-08-01 | 2020.0 | 79.0 | 63.0 | 72.0 | 69.0 | 87.0 | 81.0 | 0.0 | Leadership, Power Header | 74 | 61 | 86 | 76 | 77 | 62 | 73 | 72 | 70 | 83 | 77 | 79 | 80 | 82 | 60 | 83 | 91 | 80 | 80 | 55 | 83 | 89 | 52 | 63 | 68 | 85 | 89 | 90 | 11 | 8 | 9 | 7 | 11 | 73 | 73 | 73 | 71 | 71 | 71 | 71 | 71 | 71 | 71 | 71 | 73 | 75 | 75 | 75 | 73 | 83 | 84 | 84 | 84 | 83 | 85 | 87 | 87 | 87 | 85 |
| 156616 | F. Ribéry | 32 | 1983-04-07 | 170 | 72 | France | FC Bayern München | 87 | 87 | 34000000 | LM | Right | 4 | 4 | 5 | High/Medium | Normal | Yes | SUB | 7.0 | 2007-07-01 | 2017.0 | 87.0 | 77.0 | 84.0 | 91.0 | 25.0 | 59.0 | 0.0 | Injury Prone, Flair, Technical Dribbler (CPU A... | 81 | 77 | 41 | 85 | 79 | 90 | 82 | 81 | 72 | 91 | 85 | 85 | 88 | 85 | 90 | 76 | 49 | 56 | 60 | 73 | 52 | 36 | 83 | 88 | 80 | 1 | 25 | 26 | 15 | 6 | 9 | 7 | 10 | 80 | 80 | 80 | 88 | 87 | 87 | 87 | 88 | 88 | 88 | 88 | 87 | 80 | 80 | 80 | 87 | 64 | 60 | 60 | 60 | 64 | 58 | 45 | 45 | 45 | 58 |
| 162895 | Cesc Fàbregas | 28 | 1987-05-04 | 175 | 74 | Spain | Chelsea | 87 | 87 | 43500000 | CM, CAM | Right | 4 | 3 | 3 | High/Medium | Normal | Yes | RDM | 4.0 | 2014-07-01 | 2019.0 | 63.0 | 78.0 | 90.0 | 81.0 | 64.0 | 65.0 | 0.0 | Long Passer (CPU AI Only), Playmaker (CPU AI O... | 89 | 78 | 74 | 94 | 81 | 81 | 77 | 81 | 93 | 87 | 63 | 56 | 65 | 83 | 77 | 77 | 68 | 79 | 64 | 78 | 45 | 61 | 79 | 93 | 80 | 62 | 66 | 62 | 6 | 10 | 8 | 15 | 15 | 80 | 80 | 80 | 83 | 84 | 84 | 84 | 83 | 86 | 86 | 86 | 85 | 87 | 87 | 87 | 85 | 77 | 77 | 77 | 77 | 77 | 74 | 69 | 69 | 69 | 74 |
| 195864 | P. Pogba | 22 | 1993-03-15 | 188 | 80 | France | Juventus | 86 | 92 | 53000000 | CM | Right | 4 | 4 | 4 | High/Medium | Lean | Yes | LCM | 10.0 | 2012-07-01 | 2019.0 | 77.0 | 80.0 | 82.0 | 86.0 | 74.0 | 88.0 | 0.0 | Dives Into Tackles (CPU AI Only), Flair, Long ... | 79 | 70 | 60 | 85 | 84 | 88 | 81 | 95 | 82 | 88 | 75 | 81 | 71 | 93 | 62 | 93 | 83 | 87 | 92 | 91 | 78 | 73 | 87 | 87 | 85 | 80 | 76 | 85 | 5 | 6 | 2 | 4 | 3 | 84 | 84 | 84 | 85 | 86 | 86 | 86 | 85 | 86 | 86 | 86 | 85 | 86 | 86 | 86 | 85 | 82 | 83 | 83 | 83 | 82 | 81 | 81 | 81 | 81 | 81 |
| 193080 | De Gea | 24 | 1990-11-07 | 193 | 82 | Spain | Manchester United | 86 | 89 | 42500000 | GK | Right | 3 | 3 | 1 | Medium/Medium | Lean | Yes | GK | 1.0 | 2011-07-01 | 2019.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 53.0 | Puncher | 9 | 1 | 21 | 31 | 1 | 1 | 21 | 13 | 32 | 31 | 51 | 55 | 57 | 79 | 43 | 31 | 67 | 25 | 64 | -1 | 38 | 30 | -1 | 9 | 40 | 1 | 21 | 1 | 88 | 81 | 86 | 85 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 165153 | K. Benzema | 27 | 1987-12-19 | 187 | 79 | France | Real Madrid | 86 | 88 | 46500000 | ST | Right | 4 | 4 | 4 | Medium/Low | Normal | Yes | ST | 9.0 | 2009-07-01 | 2019.0 | 83.0 | 84.0 | 76.0 | 81.0 | 22.0 | 75.0 | 0.0 | Beat Offside Trap, Finesse Shot | 75 | 88 | 78 | 88 | 77 | 82 | 79 | 73 | 47 | 86 | 81 | 84 | 71 | 83 | 49 | 83 | 69 | 78 | 78 | 78 | 65 | 22 | 87 | 85 | 82 | 1 | 5 | -1 | 13 | 11 | 5 | 5 | 7 | 86 | 86 | 86 | 85 | 86 | 86 | 86 | 85 | 85 | 85 | 85 | 83 | 74 | 74 | 74 | 83 | 58 | 54 | 54 | 54 | 58 | 54 | 46 | 46 | 46 | 54 |
| 178603 | M. Hummels | 26 | 1988-12-16 | 191 | 92 | Germany | Borussia Dortmund | 86 | 88 | 39000000 | CB | Right | 4 | 3 | 2 | High/Medium | Normal | Yes | LCB | 15.0 | 2009-07-01 | 2017.0 | 64.0 | 58.0 | 74.0 | 70.0 | 88.0 | 77.0 | 0.0 | Avoids Using Weaker Foot, Leadership, Long Pas... | 64 | 53 | 89 | 76 | 69 | 68 | 68 | 45 | 84 | 77 | 61 | 65 | 56 | 85 | 57 | 71 | 70 | 67 | 86 | 51 | 71 | 92 | 56 | 80 | 64 | 88 | 84 | 86 | 15 | 6 | 10 | 5 | 6 | 71 | 71 | 71 | 70 | 71 | 71 | 71 | 70 | 73 | 73 | 73 | 72 | 78 | 78 | 78 | 72 | 80 | 84 | 84 | 84 | 80 | 82 | 86 | 86 | 86 | 82 |
| 184941 | A. Sánchez | 26 | 1988-12-19 | 169 | 62 | Chile | Arsenal | 86 | 88 | 47000000 | LW, RW | Right | 4 | 3 | 4 | High/High | Normal | Yes | LW | 17.0 | 2014-07-10 | 2019.0 | 87.0 | 83.0 | 78.0 | 88.0 | 39.0 | 73.0 | 0.0 | Flair, Technical Dribbler (CPU AI Only) | 76 | 85 | 60 | 80 | 78 | 88 | 78 | 77 | 73 | 87 | 88 | 85 | 91 | 84 | 87 | 82 | 72 | 86 | 64 | 80 | 84 | 42 | 87 | 78 | 81 | 30 | 39 | 35 | 10 | 10 | 15 | 12 | 13 | 83 | 83 | 83 | 86 | 86 | 86 | 86 | 86 | 85 | 85 | 85 | 85 | 80 | 80 | 80 | 85 | 68 | 65 | 65 | 65 | 68 | 64 | 55 | 55 | 55 | 64 |
| 188350 | M. Reus | 26 | 1989-05-31 | 180 | 75 | Germany | Borussia Dortmund | 86 | 88 | 45500000 | LM, CAM | Right | 3 | 4 | 4 | Medium/Medium | Lean | Yes | SUB | 11.0 | 2012-07-01 | 2019.0 | 90.0 | 84.0 | 85.0 | 86.0 | 39.0 | 64.0 | 0.0 | Injury Prone, Speed Dribbler (CPU AI Only) | 89 | 81 | 51 | 88 | 78 | 86 | 90 | 87 | 78 | 85 | 89 | 90 | 89 | 87 | 82 | 83 | 70 | 70 | 65 | 83 | 47 | 63 | 87 | 83 | 89 | 35 | 39 | 48 | 12 | 12 | 13 | 13 | 11 | 82 | 82 | 82 | 87 | 86 | 86 | 86 | 87 | 87 | 87 | 87 | 86 | 81 | 81 | 81 | 86 | 69 | 65 | 65 | 65 | 69 | 65 | 53 | 53 | 53 | 65 |
| 189511 | Sergio Busquets | 26 | 1988-07-16 | 189 | 76 | Spain | FC Barcelona | 86 | 88 | 39000000 | CDM | Right | 4 | 3 | 3 | Medium/Medium | Lean | Yes | CDM | 5.0 | 2008-09-01 | 2019.0 | 53.0 | 59.0 | 78.0 | 75.0 | 83.0 | 81.0 | 0.0 | Diver | 62 | 60 | 70 | 87 | 44 | 74 | 66 | 68 | 80 | 83 | 53 | 48 | 57 | 82 | 55 | 61 | 66 | 86 | 77 | 54 | 86 | 86 | 77 | 85 | 60 | 88 | 88 | 81 | 5 | 8 | 13 | 9 | 13 | 71 | 71 | 71 | 73 | 75 | 75 | 75 | 73 | 78 | 78 | 78 | 76 | 83 | 83 | 83 | 76 | 80 | 86 | 86 | 86 | 80 | 80 | 83 | 83 | 83 | 80 |
| 189596 | T. Müller | 25 | 1989-09-13 | 186 | 75 | Germany | FC Bayern München | 86 | 88 | 47500000 | CF, RM, CAM, ST | Right | 4 | 4 | 3 | High/High | Lean | Yes | CF | 25.0 | 2008-08-10 | 2019.0 | 77.0 | 84.0 | 80.0 | 79.0 | 46.0 | 72.0 | 0.0 | Injury Free, Finesse Shot, Outside Foot Shot | 74 | 90 | 84 | 81 | 83 | 78 | 81 | 58 | 77 | 80 | 73 | 73 | 72 | 91 | 73 | 70 | 81 | 90 | 68 | 80 | 57 | 58 | 94 | 85 | 91 | 32 | 41 | 44 | 6 | 7 | 11 | 14 | 14 | 85 | 85 | 85 | 85 | 86 | 86 | 86 | 85 | 85 | 85 | 85 | 85 | 82 | 82 | 82 | 85 | 71 | 68 | 68 | 68 | 71 | 68 | 60 | 60 | 60 | 68 |
| 179844 | Diego Costa | 26 | 1988-10-07 | 188 | 85 | Spain | Chelsea | 86 | 87 | 46500000 | ST | Right | 4 | 4 | 3 | High/Medium | Normal | Yes | ST | 19.0 | 2014-07-15 | 2019.0 | 82.0 | 83.0 | 63.0 | 79.0 | 40.0 | 88.0 | 0.0 | Diver | 63 | 90 | 82 | 65 | 81 | 82 | 58 | 59 | 48 | 82 | 79 | 85 | 63 | 85 | 42 | 83 | 60 | 85 | 91 | 69 | 90 | 40 | 89 | 74 | 76 | 28 | 39 | 34 | 11 | 13 | 12 | 8 | 11 | 86 | 86 | 86 | 81 | 83 | 83 | 83 | 81 | 79 | 79 | 79 | 79 | 72 | 72 | 72 | 79 | 63 | 61 | 61 | 61 | 63 | 61 | 59 | 59 | 59 | 61 |
| 54050 | W. Rooney | 29 | 1985-10-24 | 176 | 83 | England | Manchester United | 86 | 86 | 38500000 | ST, CM, CAM | Right | 4 | 4 | 3 | Medium/High | Stocky | Yes | CAM | 10.0 | 2004-08-31 | 2019.0 | 75.0 | 86.0 | 81.0 | 81.0 | 47.0 | 87.0 | 0.0 | Leadership, Long Shot Taker (CPU AI Only) | 78 | 86 | 80 | 80 | 89 | 80 | 84 | 84 | 85 | 85 | 74 | 73 | 77 | 90 | 77 | 88 | 82 | 89 | 85 | 85 | 89 | 69 | 89 | 83 | 81 | 29 | 54 | 37 | 10 | 11 | 13 | 8 | 7 | 86 | 86 | 86 | 84 | 85 | 85 | 85 | 84 | 85 | 85 | 85 | 84 | 83 | 83 | 83 | 84 | 70 | 71 | 71 | 71 | 70 | 67 | 64 | 64 | 64 | 67 |
| 121944 | B. Schweinsteiger | 30 | 1984-08-01 | 183 | 79 | Germany | Manchester United | 86 | 86 | 35000000 | CM, CDM | Right | 4 | 3 | 3 | High/High | Normal | Yes | SUB | 31.0 | 2015-07-13 | 2018.0 | 52.0 | 79.0 | 85.0 | 79.0 | 77.0 | 78.0 | 0.0 | Injury Prone, Playmaker (CPU AI Only) | 77 | 76 | 79 | 86 | 81 | 77 | 82 | 78 | 87 | 80 | 44 | 42 | 72 | 87 | 47 | 82 | 84 | 66 | 78 | 79 | 80 | 86 | 78 | 86 | 85 | 67 | 76 | 75 | 14 | 14 | 13 | 13 | 11 | 80 | 80 | 80 | 80 | 81 | 81 | 81 | 80 | 83 | 83 | 83 | 80 | 86 | 86 | 86 | 80 | 80 | 84 | 84 | 84 | 80 | 79 | 81 | 81 | 81 | 79 |
| 143001 | C. Tévez | 31 | 1984-02-05 | 173 | 71 | Argentina | Boca Juniors | 86 | 86 | 34500000 | ST, CF | Right | 3 | 3 | 3 | High/High | Stocky | Yes | CAM | 10.0 | 2015-07-13 | 2018.0 | 87.0 | 88.0 | 76.0 | 88.0 | 45.0 | 84.0 | 0.0 | Finesse Shot, Long Shot Taker (CPU AI Only), O... | 73 | 89 | 60 | 76 | 88 | 88 | 84 | 86 | 58 | 91 | 93 | 85 | 83 | 92 | 91 | 94 | 81 | 93 | 81 | 92 | 85 | 34 | 92 | 77 | 81 | 43 | 51 | 51 | 4 | 2 | 3 | 2 | 4 | 86 | 86 | 86 | 87 | 88 | 88 | 88 | 87 | 86 | 86 | 86 | 85 | 79 | 79 | 79 | 85 | 70 | 67 | 67 | 67 | 70 | 67 | 61 | 61 | 61 | 67 |
| 181872 | A. Vidal | 28 | 1987-05-22 | 180 | 75 | Chile | FC Bayern München | 86 | 86 | 37500000 | CM, CAM, CDM | Right | 4 | 4 | 3 | High/High | Normal | Yes | RDM | 23.0 | 2015-07-28 | 2019.0 | 77.0 | 79.0 | 80.0 | 81.0 | 83.0 | 83.0 | 0.0 | Dives Into Tackles (CPU AI Only), Long Shot Ta... | 76 | 74 | 76 | 83 | 78 | 82 | 76 | 71 | 81 | 83 | 77 | 75 | 71 | 90 | 76 | 86 | 83 | 93 | 75 | 82 | 91 | 90 | 81 | 84 | 86 | 77 | 92 | 88 | 4 | 2 | 4 | 2 | 4 | 82 | 82 | 82 | 82 | 83 | 83 | 83 | 82 | 83 | 83 | 83 | 83 | 86 | 86 | 86 | 83 | 86 | 87 | 87 | 87 | 86 | 86 | 85 | 85 | 85 | 86 |
| 152729 | Piqué | 28 | 1987-02-02 | 193 | 85 | Spain | FC Barcelona | 85 | 86 | 29500000 | CB | Right | 4 | 3 | 2 | High/Medium | Normal | Yes | RCB | 3.0 | 2008-07-01 | 2019.0 | 64.0 | 61.0 | 70.0 | 64.0 | 86.0 | 76.0 | 0.0 | Long Passer (CPU AI Only) | 64 | 70 | 82 | 82 | 57 | 68 | 73 | 43 | 76 | 76 | 56 | 67 | 57 | 81 | 44 | 71 | 74 | 69 | 83 | 60 | 68 | 88 | 66 | 62 | 69 | 86 | 87 | 83 | 10 | 11 | 14 | 15 | 8 | 70 | 70 | 70 | 68 | 69 | 69 | 69 | 68 | 70 | 70 | 70 | 70 | 76 | 76 | 76 | 70 | 78 | 83 | 83 | 83 | 78 | 80 | 85 | 85 | 85 | 80 |
| 13732 | J. Terry | 34 | 1980-12-07 | 187 | 90 | England | Chelsea | 85 | 85 | 10500000 | CB | Right | 3 | 4 | 2 | Medium/High | Normal | Yes | SUB | 26.0 | 1998-10-28 | 2016.0 | 34.0 | 47.0 | 57.0 | 52.0 | 87.0 | 81.0 | 0.0 | Leadership, Power Header, One Club Player | 42 | 46 | 90 | 69 | 55 | 45 | 44 | 31 | 64 | 65 | 32 | 24 | 44 | 85 | 46 | 61 | 81 | 67 | 88 | 33 | 88 | 89 | 38 | 59 | 52 | 89 | 89 | 87 | 14 | 5 | 6 | 15 | 8 | 58 | 58 | 58 | 51 | 55 | 55 | 55 | 51 | 56 | 56 | 56 | 54 | 64 | 64 | 64 | 54 | 69 | 78 | 78 | 78 | 69 | 72 | 85 | 85 | 85 | 72 |
| 146562 | Santi Cazorla | 30 | 1984-12-13 | 168 | 66 | Spain | Arsenal | 85 | 85 | 31000000 | CAM, CM, LM | Right | 3 | 5 | 4 | Medium/Medium | Stocky | Yes | LCM | 19.0 | 2012-08-07 | 2017.0 | 73.0 | 78.0 | 85.0 | 87.0 | 57.0 | 64.0 | 0.0 | Flair, Long Shot Taker (CPU AI Only), Technica... | 91 | 77 | 53 | 86 | 67 | 87 | 86 | 78 | 84 | 86 | 79 | 68 | 86 | 85 | 91 | 78 | 71 | 79 | 60 | 82 | 63 | 55 | 82 | 85 | 80 | 58 | 62 | 48 | 6 | 9 | 5 | 7 | 15 | 78 | 78 | 78 | 84 | 83 | 83 | 83 | 84 | 85 | 85 | 85 | 84 | 82 | 82 | 82 | 84 | 72 | 72 | 72 | 72 | 72 | 69 | 62 | 62 | 62 | 69 |
| 167948 | H. Lloris | 28 | 1986-12-26 | 188 | 78 | France | Tottenham Hotspur | 85 | 85 | 29000000 | GK | Left | 3 | 1 | 1 | Medium/Medium | Lean | Yes | GK | 1.0 | 2012-08-01 | 2019.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 64.0 | Puncher | 1 | -5 | -5 | 27 | -3 | -5 | -3 | -5 | 31 | 34 | 65 | 63 | 55 | 84 | 54 | 23 | 74 | 41 | 43 | 3 | 31 | 27 | -5 | 7 | 40 | -1 | -5 | 11 | 88 | 82 | 68 | 81 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 171919 | Naldo | 32 | 1982-09-10 | 198 | 89 | Brazil | VfL Wolfsburg | 85 | 85 | 19500000 | CB | Right | 3 | 4 | 2 | High/Medium | Lean | No | RCB | 25.0 | 2012-07-19 | 2016.0 | 73.0 | 69.0 | 64.0 | 63.0 | 88.0 | 76.0 | 0.0 | Power Free-Kick, Power Header | 51 | 63 | 93 | 76 | 59 | 60 | 45 | 76 | 77 | 70 | 67 | 79 | 59 | 82 | 42 | 92 | 49 | 64 | 86 | 89 | 76 | 100 | 53 | 60 | 61 | 87 | 90 | 88 | 14 | 10 | 14 | 8 | 14 | 73 | 73 | 73 | 65 | 69 | 69 | 69 | 65 | 68 | 68 | 68 | 65 | 72 | 72 | 72 | 65 | 76 | 80 | 80 | 80 | 76 | 79 | 85 | 85 | 85 | 79 |
| 183898 | A. Di María | 27 | 1988-02-14 | 180 | 75 | Argentina | Paris Saint-Germain | 85 | 85 | 34000000 | CAM, CM, RM | Left | 4 | 2 | 4 | High/Medium | Lean | Yes | LW | 11.0 | 2015-08-06 | 2019.0 | 88.0 | 79.0 | 83.0 | 86.0 | 49.0 | 70.0 | 0.0 | Diver, Avoids Using Weaker Foot, Dives Into Ta... | 89 | 75 | 53 | 81 | 77 | 87 | 83 | 72 | 81 | 86 | 88 | 88 | 90 | 80 | 79 | 87 | 72 | 78 | 64 | 79 | 76 | 29 | 84 | 83 | 73 | 37 | 63 | 61 | 10 | 7 | 11 | 12 | 11 | 81 | 81 | 81 | 86 | 85 | 85 | 85 | 86 | 85 | 85 | 85 | 86 | 81 | 81 | 81 | 86 | 73 | 69 | 69 | 69 | 73 | 70 | 61 | 61 | 61 | 70 |
| 197781 | Isco | 23 | 1992-04-21 | 176 | 74 | Spain | Real Madrid | 84 | 89 | 39000000 | CAM, CM, LM | Right | 3 | 3 | 4 | High/Medium | Normal | Yes | SUB | 22.0 | 2013-07-03 | 2018.0 | 75.0 | 76.0 | 81.0 | 87.0 | 40.0 | 63.0 | 0.0 | Selfish, Finesse Shot, Playmaker (CPU AI Only)... | 72 | 76 | 57 | 83 | 69 | 87 | 88 | 74 | 77 | 89 | 76 | 72 | 80 | 79 | 84 | 71 | 64 | 77 | 59 | 80 | 58 | 53 | 75 | 83 | 76 | 25 | 63 | 48 | 10 | 8 | 12 | 15 | 6 | 77 | 77 | 77 | 82 | 82 | 82 | 82 | 82 | 84 | 84 | 84 | 82 | 80 | 80 | 80 | 82 | 65 | 64 | 64 | 64 | 65 | 61 | 53 | 53 | 53 | 61 |
| 199556 | M. Verratti | 22 | 1992-11-05 | 165 | 60 | Italy | Paris Saint-Germain | 84 | 89 | 38000000 | CM, CDM | Right | 3 | 4 | 4 | High/High | Normal | Yes | RCM | 6.0 | 2012-07-01 | 2019.0 | 68.0 | 58.0 | 83.0 | 87.0 | 78.0 | 69.0 | 0.0 | Long Passer (CPU AI Only), Playmaker (CPU AI O... | 74 | 58 | 55 | 91 | 64 | 86 | 70 | 62 | 86 | 85 | 70 | 57 | 87 | 90 | 94 | 66 | 64 | 79 | 49 | 58 | 89 | 87 | 71 | 84 | 64 | 78 | 84 | 74 | 12 | 12 | 15 | 15 | 10 | 70 | 70 | 70 | 78 | 77 | 77 | 77 | 78 | 81 | 81 | 81 | 80 | 84 | 84 | 84 | 80 | 81 | 84 | 84 | 84 | 81 | 79 | 77 | 77 | 77 | 79 |
| 188152 | Oscar | 23 | 1991-09-09 | 179 | 67 | Brazil | Chelsea | 84 | 88 | 38000000 | CAM, RM | Right | 3 | 3 | 4 | Medium/High | Lean | Yes | CAM | 8.0 | 2012-07-26 | 2019.0 | 79.0 | 75.0 | 81.0 | 84.0 | 43.0 | 47.0 | 0.0 | Beat Offside Trap, Long Shot Taker (CPU AI Onl... | 70 | 75 | 54 | 85 | 63 | 84 | 77 | 77 | 82 | 85 | 80 | 75 | 86 | 81 | 80 | 76 | 66 | 76 | 36 | 77 | 31 | 34 | 81 | 85 | 68 | 37 | 50 | 47 | 12 | 10 | 15 | 12 | 12 | 76 | 76 | 76 | 82 | 82 | 82 | 82 | 82 | 84 | 84 | 84 | 82 | 80 | 80 | 80 | 82 | 66 | 63 | 63 | 63 | 66 | 63 | 50 | 50 | 50 | 63 |
| 189242 | Coutinho | 23 | 1992-06-12 | 171 | 68 | Brazil | Liverpool | 84 | 88 | 38000000 | CAM, LW, CM | Right | 3 | 4 | 4 | High/Low | Lean | Yes | CAM | 10.0 | 2013-01-30 | 2020.0 | 82.0 | 74.0 | 81.0 | 87.0 | 33.0 | 56.0 | 0.0 | Finesse Shot, Flair, Long Shot Taker (CPU AI O... | 74 | 70 | 47 | 85 | 75 | 87 | 84 | 72 | 78 | 88 | 89 | 79 | 91 | 75 | 91 | 87 | 59 | 73 | 61 | 87 | 35 | 30 | 76 | 87 | 64 | 24 | 38 | 35 | 12 | 7 | 9 | 14 | 6 | 75 | 75 | 75 | 83 | 82 | 82 | 82 | 83 | 84 | 84 | 84 | 82 | 78 | 78 | 78 | 82 | 63 | 59 | 59 | 59 | 63 | 58 | 46 | 46 | 46 | 58 |
| 189332 | Jordi Alba | 26 | 1989-03-21 | 170 | 68 | Spain | FC Barcelona | 84 | 88 | 30000000 | LB | Left | 3 | 3 | 3 | High/Medium | Normal | Yes | LB | 18.0 | 2012-07-01 | 2020.0 | 92.0 | 69.0 | 75.0 | 82.0 | 80.0 | 75.0 | 0.0 | NaN | 83 | 73 | 58 | 76 | 60 | 80 | 77 | 64 | 69 | 85 | 93 | 92 | 89 | 79 | 87 | 64 | 81 | 88 | 67 | 66 | 73 | 82 | 77 | 68 | 59 | 82 | 86 | 85 | 13 | 15 | 13 | 6 | 13 | 76 | 76 | 76 | 81 | 78 | 78 | 78 | 81 | 79 | 79 | 79 | 81 | 78 | 78 | 78 | 81 | 84 | 80 | 80 | 80 | 84 | 84 | 79 | 79 | 79 | 84 |
| 192318 | M. Götze | 23 | 1992-06-03 | 176 | 72 | Germany | FC Bayern München | 84 | 88 | 38000000 | CAM, LM, CF, CM | Right | 3 | 4 | 4 | Medium/Medium | Normal | Yes | SUB | 19.0 | 2013-07-01 | 2017.0 | 72.0 | 73.0 | 81.0 | 88.0 | 32.0 | 62.0 | 0.0 | Flair, Technical Dribbler (CPU AI Only) | 76 | 75 | 53 | 84 | 83 | 86 | 80 | 74 | 74 | 90 | 73 | 61 | 87 | 83 | 83 | 66 | 70 | 65 | 64 | 58 | 48 | 53 | 82 | 83 | 65 | 7 | 29 | 26 | 14 | 7 | 12 | 6 | 10 | 78 | 78 | 78 | 83 | 83 | 83 | 83 | 83 | 84 | 84 | 84 | 83 | 79 | 79 | 79 | 83 | 63 | 61 | 61 | 61 | 63 | 58 | 48 | 48 | 48 | 58 |
| 192563 | B. Leno | 23 | 1992-03-04 | 190 | 82 | Germany | Bayer 04 Leverkusen | 84 | 88 | 32500000 | GK | Right | 2 | 4 | 1 | Medium/Medium | Lean | No | GK | 1.0 | 2012-01-01 | 2018.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 49.0 | NaN | -7 | -7 | 1 | 38 | -5 | 7 | -7 | -9 | 33 | 16 | 46 | 52 | 52 | 85 | 44 | 3 | 73 | 43 | 68 | 3 | 28 | 22 | -11 | 81 | 23 | -9 | 5 | 11 | 85 | 86 | 81 | 83 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 193301 | A. Lacazette | 24 | 1991-05-28 | 175 | 73 | France | Olympique Lyonnais | 84 | 88 | 39000000 | ST, RW | Right | 2 | 4 | 4 | High/Medium | Normal | Yes | RS | 10.0 | 2009-07-01 | 2019.0 | 88.0 | 85.0 | 74.0 | 85.0 | 34.0 | 73.0 | 0.0 | Diver, Flair, Speed Dribbler (CPU AI Only) | 68 | 90 | 77 | 82 | 85 | 85 | 74 | 73 | 72 | 85 | 88 | 88 | 89 | 89 | 83 | 87 | 81 | 71 | 74 | 78 | 70 | 40 | 88 | 76 | 82 | 21 | 29 | 30 | 11 | 6 | 9 | 5 | 6 | 84 | 84 | 84 | 83 | 84 | 84 | 84 | 83 | 82 | 82 | 82 | 81 | 75 | 75 | 75 | 81 | 61 | 59 | 59 | 59 | 61 | 58 | 52 | 52 | 52 | 58 |
| 191180 | J. Pastore | 26 | 1989-06-20 | 187 | 78 | Argentina | Paris Saint-Germain | 84 | 87 | 34500000 | CM, LW, RW | Right | 3 | 4 | 4 | High/Medium | Lean | Yes | SUB | 27.0 | 2011-08-01 | 2019.0 | 74.0 | 77.0 | 84.0 | 86.0 | 58.0 | 68.0 | 0.0 | Flair, Playmaker (CPU AI Only), Technical Drib... | 78 | 75 | 63 | 88 | 78 | 91 | 83 | 69 | 84 | 88 | 70 | 75 | 85 | 89 | 58 | 78 | 49 | 72 | 66 | 78 | 65 | 71 | 82 | 90 | 75 | 49 | 62 | 51 | 14 | 9 | 6 | 10 | 12 | 79 | 79 | 79 | 83 | 83 | 83 | 83 | 83 | 85 | 85 | 85 | 83 | 84 | 84 | 84 | 83 | 73 | 74 | 74 | 74 | 73 | 70 | 65 | 65 | 65 | 70 |
| 167664 | G. Higuaín | 27 | 1987-12-10 | 184 | 82 | Argentina | Napoli | 84 | 86 | 34500000 | ST | Right | 3 | 4 | 3 | High/Medium | Normal | Yes | LS | 9.0 | 2013-07-27 | 2018.0 | 80.0 | 85.0 | 68.0 | 81.0 | 22.0 | 71.0 | 0.0 | Outside Foot Shot | 72 | 89 | 78 | 70 | 85 | 83 | 72 | 62 | 55 | 85 | 79 | 80 | 72 | 84 | 60 | 85 | 73 | 68 | 80 | 78 | 50 | 13 | 89 | 70 | 71 | 5 | 5 | -5 | 5 | 12 | 7 | 5 | 10 | 84 | 84 | 84 | 81 | 83 | 83 | 83 | 81 | 79 | 79 | 79 | 79 | 69 | 69 | 69 | 79 | 54 | 49 | 49 | 49 | 54 | 50 | 43 | 43 | 43 | 50 |
| 170890 | B. Matuidi | 28 | 1987-04-09 | 175 | 70 | France | Paris Saint-Germain | 84 | 85 | 25500000 | CDM, CM | Left | 3 | 3 | 2 | High/High | Lean | Yes | LCM | 14.0 | 2011-07-01 | 2018.0 | 75.0 | 67.0 | 77.0 | 75.0 | 83.0 | 81.0 | 0.0 | Injury Free, Team Player | 75 | 65 | 73 | 84 | 72 | 73 | 65 | 52 | 79 | 77 | 71 | 73 | 73 | 83 | 79 | 74 | 85 | 94 | 71 | 66 | 84 | 86 | 70 | 74 | 56 | 82 | 84 | 83 | 8 | 11 | 5 | 10 | 14 | 74 | 74 | 74 | 76 | 75 | 75 | 75 | 76 | 77 | 77 | 77 | 78 | 80 | 80 | 80 | 78 | 83 | 84 | 84 | 84 | 83 | 83 | 82 | 82 | 82 | 83 |
| 177610 | Javi Martinez | 26 | 1988-09-02 | 190 | 81 | Spain | FC Bayern München | 84 | 85 | 27500000 | CB, CDM, CM | Right | 3 | 3 | 2 | Medium/Medium | Normal | Yes | SUB | 8.0 | 2012-08-29 | 2017.0 | 52.0 | 61.0 | 71.0 | 65.0 | 84.0 | 79.0 | 0.0 | Power Header | 60 | 55 | 86 | 77 | 58 | 61 | 61 | 36 | 76 | 69 | 47 | 53 | 49 | 80 | 59 | 81 | 70 | 52 | 83 | 63 | 88 | 85 | 57 | 75 | 46 | 83 | 86 | 87 | 5 | 12 | 9 | 11 | 8 | 69 | 69 | 69 | 65 | 68 | 68 | 68 | 65 | 69 | 69 | 69 | 67 | 74 | 74 | 74 | 67 | 74 | 81 | 81 | 81 | 74 | 76 | 84 | 84 | 84 | 76 |
| 1179 | G. Buffon | 37 | 1978-01-28 | 191 | 83 | Italy | Juventus | 84 | 84 | 9000000 | GK | Right | 4 | 2 | 1 | Medium/Medium | Normal | Yes | GK | 1.0 | 2001-07-01 | 2017.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 46.0 | Leadership, One Club Player, Team Player | 1 | 5 | -6 | 37 | 9 | 30 | 15 | 1 | 39 | 18 | 53 | 53 | 59 | 78 | 49 | -7 | 75 | 39 | 66 | 1 | 42 | 11 | 3 | 75 | 9 | -5 | -3 | -3 | 86 | 81 | 80 | 88 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5479 | Casillas | 34 | 1981-05-20 | 185 | 84 | Spain | FC Porto | 84 | 84 | 19000000 | GK | Left | 4 | 2 | 1 | Medium/Medium | Normal | Yes | GK | 12.0 | 2015-07-12 | 2017.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 64.0 | GK Long Throw, 1-on-1 Rush | 1 | -1 | 25 | 21 | 3 | 25 | 25 | 1 | 22 | 20 | 65 | 63 | 62 | 80 | 46 | 30 | 77 | 43 | 70 | 1 | 23 | 19 | 7 | 105 | 24 | -3 | -3 | 1 | 87 | 79 | 67 | 79 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7763 | A. Pirlo | 36 | 1979-05-19 | 177 | 68 | Italy | New York City FC | 84 | 84 | 2400000 | CM, CDM | Right | 4 | 4 | 2 | Low/Low | Lean | Yes | CDM | 21.0 | 2015-07-06 | 2021.0 | 41.0 | 69.0 | 93.0 | 79.0 | 52.0 | 58.0 | 0.0 | Long Passer (CPU AI Only), Playmaker (CPU AI O... | 104 | 56 | 54 | 93 | 76 | 80 | 94 | 93 | 94 | 89 | 45 | 33 | 28 | 88 | 61 | 78 | 33 | 61 | 59 | 82 | 52 | 59 | 48 | 94 | 83 | 40 | 48 | 46 | 5 | 4 | 1 | 5 | 2 | 69 | 69 | 69 | 76 | 76 | 76 | 76 | 76 | 81 | 81 | 81 | 79 | 84 | 84 | 84 | 79 | 70 | 73 | 73 | 73 | 70 | 65 | 59 | 59 | 59 | 65 |
| 7826 | R. van Persie | 31 | 1983-08-06 | 187 | 71 | Netherlands | Fenerbahçe SK | 84 | 84 | 25500000 | ST | Left | 4 | 3 | 4 | Medium/Low | Normal | Yes | LS | 11.0 | 2015-07-14 | 2018.0 | 67.0 | 86.0 | 81.0 | 81.0 | 33.0 | 67.0 | 0.0 | Injury Prone, Flair | 81 | 85 | 74 | 82 | 92 | 82 | 86 | 81 | 75 | 83 | 63 | 69 | 70 | 84 | 59 | 89 | 59 | 63 | 72 | 82 | 55 | 34 | 85 | 82 | 84 | 23 | 32 | 21 | 9 | 10 | 5 | 7 | 8 | 84 | 84 | 84 | 83 | 84 | 84 | 84 | 83 | 84 | 84 | 84 | 82 | 78 | 78 | 78 | 82 | 61 | 60 | 60 | 60 | 61 | 57 | 51 | 51 | 51 | 57 |
| 13743 | S. Gerrard | 35 | 1980-05-30 | 183 | 83 | England | LA Galaxy | 84 | 84 | 10000000 | CM, CDM, CAM | Right | 4 | 3 | 3 | Medium/Medium | Normal | Yes | LCM | 8.0 | 2015-07-08 | 2021.0 | 54.0 | 82.0 | 86.0 | 75.0 | 69.0 | 80.0 | 0.0 | Long Passer (CPU AI Only), Long Shot Taker (CP... | 86 | 79 | 75 | 86 | 80 | 74 | 84 | 82 | 87 | 82 | 55 | 54 | 59 | 86 | 60 | 90 | 71 | 74 | 82 | 82 | 85 | 70 | 82 | 85 | 85 | 63 | 73 | 68 | 13 | 15 | 13 | 5 | 10 | 81 | 81 | 81 | 79 | 81 | 81 | 81 | 79 | 82 | 82 | 82 | 80 | 84 | 84 | 84 | 80 | 76 | 80 | 80 | 80 | 76 | 75 | 76 | 76 | 76 | 75 |
| 45197 | Xabi Alonso | 33 | 1981-11-25 | 183 | 80 | Spain | FC Bayern München | 84 | 84 | 13000000 | CDM, CM | Right | 4 | 4 | 2 | Low/Medium | Normal | Yes | CM | 14.0 | 2014-08-29 | 2016.0 | 33.0 | 69.0 | 88.0 | 77.0 | 76.0 | 70.0 | 0.0 | Long Passer (CPU AI Only), Playmaker (CPU AI O... | 79 | 54 | 72 | 93 | 71 | 77 | 82 | 84 | 94 | 84 | 15 | 18 | 52 | 86 | 66 | 81 | 45 | 60 | 73 | 82 | 78 | 87 | 75 | 88 | 80 | 68 | 76 | 68 | 7 | 8 | 10 | 10 | 10 | 73 | 73 | 73 | 75 | 77 | 77 | 77 | 75 | 80 | 80 | 80 | 76 | 86 | 86 | 86 | 76 | 76 | 84 | 84 | 84 | 76 | 74 | 78 | 78 | 78 | 74 |
| 120533 | Pepe | 32 | 1983-02-26 | 188 | 81 | Portugal | Real Madrid | 84 | 84 | 17000000 | CB | Right | 3 | 3 | 2 | Medium/High | Lean | Yes | RCB | 3.0 | 2007-07-01 | 2017.0 | 73.0 | 51.0 | 58.0 | 59.0 | 86.0 | 82.0 | 0.0 | Dives Into Tackles (CPU AI Only) | 46 | 46 | 80 | 67 | 23 | 58 | 44 | 47 | 60 | 57 | 67 | 77 | 57 | 75 | 58 | 63 | 74 | 69 | 83 | 56 | 94 | 87 | 40 | 58 | 57 | 86 | 90 | 90 | 8 | 15 | 5 | 9 | 10 | 61 | 61 | 61 | 59 | 60 | 60 | 60 | 59 | 60 | 60 | 60 | 60 | 64 | 64 | 64 | 60 | 74 | 76 | 76 | 76 | 74 | 77 | 84 | 84 | 84 | 77 |
| 137186 | A. Barzagli | 34 | 1981-05-08 | 186 | 79 | Italy | Juventus | 84 | 84 | 9000000 | CB | Right | 3 | 3 | 2 | Low/High | Normal | Yes | SUB | 15.0 | 2011-01-01 | 2016.0 | 75.0 | 36.0 | 55.0 | 59.0 | 88.0 | 77.0 | 0.0 | Injury Prone, Leadership | 42 | 32 | 76 | 65 | 41 | 52 | 50 | 31 | 65 | 65 | 71 | 79 | 63 | 80 | 78 | 51 | 82 | 61 | 82 | 48 | 77 | 89 | 34 | 54 | 50 | 91 | 91 | 91 | 4 | 2 | 4 | 2 | 4 | 55 | 55 | 55 | 55 | 55 | 55 | 55 | 55 | 57 | 57 | 57 | 58 | 63 | 63 | 63 | 58 | 74 | 77 | 77 | 77 | 74 | 77 | 84 | 84 | 84 | 77 |
| 139869 | W. Sneijder | 31 | 1984-06-09 | 170 | 72 | Netherlands | Galatasaray SK | 84 | 84 | 24500000 | CAM | Right | 3 | 5 | 3 | Medium/Low | Normal | Yes | CAM | 10.0 | 2013-01-22 | 2016.0 | 74.0 | 80.0 | 86.0 | 84.0 | 45.0 | 61.0 | 0.0 | Long Passer (CPU AI Only), Long Shot Taker (CP... | 83 | 74 | 45 | 85 | 80 | 84 | 88 | 85 | 87 | 85 | 71 | 75 | 85 | 83 | 85 | 86 | 51 | 54 | 57 | 86 | 68 | 43 | 81 | 83 | 75 | 42 | 50 | 46 | 9 | 5 | 13 | 11 | 12 | 77 | 77 | 77 | 82 | 82 | 82 | 82 | 82 | 84 | 84 | 84 | 82 | 81 | 81 | 81 | 82 | 67 | 67 | 67 | 67 | 67 | 63 | 55 | 55 | 55 | 63 |
| 146530 | Dani Alves | 32 | 1983-05-06 | 172 | 70 | Brazil | FC Barcelona | 84 | 84 | 16000000 | RB | Right | 4 | 3 | 3 | High/Low | Normal | Yes | RB | 6.0 | 2008-07-01 | 2017.0 | 86.0 | 70.0 | 76.0 | 83.0 | 78.0 | 69.0 | 0.0 | Power Free-Kick, Diver | 75 | 60 | 73 | 77 | 68 | 81 | 77 | 74 | 73 | 85 | 84 | 89 | 86 | 79 | 82 | 86 | 78 | 79 | 57 | 77 | 85 | 83 | 63 | 67 | 70 | 74 | 85 | 89 | 5 | 11 | 9 | 6 | 7 | 76 | 76 | 76 | 79 | 78 | 78 | 78 | 79 | 79 | 79 | 79 | 80 | 79 | 79 | 79 | 80 | 84 | 81 | 81 | 81 | 84 | 84 | 80 | 80 | 80 | 84 |
| 150724 | J. Hart | 28 | 1987-04-19 | 196 | 91 | England | Manchester City | 84 | 84 | 25000000 | GK | Right | 3 | 3 | 1 | Medium/Medium | Normal | Yes | GK | 1.0 | 2006-05-24 | 2019.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.0 | Injury Free, Leadership, Puncher | 3 | 3 | -5 | 32 | 13 | 21 | 9 | 1 | 35 | 20 | 59 | 60 | 25 | 81 | 32 | 105 | 72 | 28 | 61 | 13 | 28 | 25 | 3 | 59 | 92 | 3 | 3 | 1 | 85 | 80 | 76 | 88 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 168609 | Miranda | 30 | 1984-09-07 | 186 | 76 | Brazil | Inter | 84 | 84 | 21000000 | CB | Right | 3 | 3 | 2 | Medium/Medium | Lean | Yes | RCB | 25.0 | 2015-07-01 | 2016.0 | 76.0 | 49.0 | 56.0 | 57.0 | 86.0 | 80.0 | 0.0 | Dives Into Tackles (CPU AI Only) | 48 | 43 | 86 | 64 | 51 | 49 | 32 | 39 | 65 | 64 | 74 | 77 | 67 | 75 | 58 | 70 | 86 | 71 | 80 | 41 | 88 | 83 | 43 | 53 | 49 | 86 | 89 | 89 | 12 | 6 | 10 | 13 | 12 | 62 | 62 | 62 | 58 | 59 | 59 | 59 | 58 | 59 | 59 | 59 | 60 | 63 | 63 | 63 | 60 | 73 | 76 | 76 | 76 | 73 | 77 | 84 | 84 | 84 | 77 |
| 173210 | C. Marchisio | 29 | 1986-01-19 | 179 | 76 | Italy | Juventus | 84 | 84 | 26500000 | CM, CDM | Right | 3 | 4 | 4 | High/High | Lean | Yes | SUB | 8.0 | 2007-06-01 | 2020.0 | 81.0 | 74.0 | 82.0 | 83.0 | 77.0 | 78.0 | 0.0 | Flair, Long Shot Taker (CPU AI Only), Technica... | 76 | 62 | 60 | 85 | 76 | 82 | 84 | 78 | 84 | 85 | 81 | 81 | 83 | 88 | 76 | 80 | 63 | 91 | 69 | 86 | 87 | 83 | 81 | 85 | 80 | 84 | 80 | 81 | 4 | 3 | 4 | 2 | 4 | 78 | 78 | 78 | 82 | 82 | 82 | 82 | 82 | 83 | 83 | 83 | 83 | 84 | 84 | 84 | 83 | 83 | 83 | 83 | 83 | 83 | 82 | 79 | 79 | 79 | 82 |
| 178088 | Juan Mata | 27 | 1988-04-28 | 170 | 63 | Spain | Manchester United | 84 | 84 | 28500000 | RM, CAM | Left | 3 | 3 | 4 | Medium/Medium | Lean | Yes | RM | 8.0 | 2014-01-25 | 2018.0 | 71.0 | 75.0 | 84.0 | 87.0 | 27.0 | 55.0 | 0.0 | Finesse Shot, Flair, Playmaker (CPU AI Only), ... | 83 | 77 | 66 | 86 | 71 | 85 | 82 | 81 | 78 | 88 | 77 | 60 | 90 | 87 | 89 | 74 | 72 | 79 | 45 | 72 | 52 | 39 | 84 | 88 | 71 | 5 | 22 | 7 | 9 | 10 | 14 | 5 | 8 | 78 | 78 | 78 | 84 | 83 | 83 | 83 | 84 | 85 | 85 | 85 | 84 | 80 | 80 | 80 | 84 | 61 | 59 | 59 | 59 | 61 | 56 | 44 | 44 | 44 | 56 |
| 189362 | Hulk | 28 | 1986-07-25 | 180 | 85 | Brazil | Zenit St. Petersburg | 84 | 84 | 28500000 | RW, ST, RM | Left | 3 | 3 | 4 | High/Medium | Stocky | Yes | RAM | 7.0 | 2012-09-03 | 2019.0 | 85.0 | 87.0 | 81.0 | 82.0 | 43.0 | 86.0 | 0.0 | Diver, Flair, Long Shot Taker (CPU AI Only), S... | 80 | 85 | 68 | 83 | 73 | 84 | 80 | 84 | 78 | 84 | 84 | 85 | 72 | 83 | 59 | 94 | 72 | 84 | 90 | 90 | 84 | 55 | 83 | 84 | 80 | 33 | 41 | 28 | 6 | 10 | 7 | 11 | 12 | 85 | 85 | 85 | 84 | 85 | 85 | 85 | 84 | 84 | 84 | 84 | 84 | 80 | 80 | 80 | 84 | 68 | 68 | 68 | 68 | 68 | 64 | 60 | 60 | 60 | 64 |
| 192366 | N. Otamendi | 27 | 1988-02-12 | 183 | 81 | Argentina | Manchester City | 84 | 84 | 24000000 | CB | Right | 3 | 3 | 2 | Medium/Medium | Normal | No | RCB | 30.0 | 2015-08-20 | 2020.0 | 75.0 | 56.0 | 56.0 | 54.0 | 87.0 | 82.0 | 0.0 | Injury Free, Dives Into Tackles (CPU AI Only),... | 50 | 47 | 88 | 59 | 58 | 26 | 50 | 34 | 68 | 57 | 74 | 76 | 71 | 80 | 51 | 69 | 90 | 78 | 79 | 52 | 88 | 86 | 43 | 28 | 44 | 89 | 87 | 91 | 12 | 5 | 8 | 11 | 12 | 65 | 65 | 65 | 59 | 60 | 60 | 60 | 59 | 59 | 59 | 59 | 60 | 64 | 64 | 64 | 60 | 75 | 77 | 77 | 77 | 75 | 79 | 84 | 84 | 84 | 79 |
| 196144 | J. Martínez | 28 | 1986-10-03 | 185 | 85 | Colombia | Atlético Madrid | 84 | 84 | 29500000 | ST | Right | 3 | 4 | 4 | High/Medium | Normal | No | RS | 11.0 | 2015-07-16 | 2017.0 | 81.0 | 82.0 | 67.0 | 79.0 | 44.0 | 85.0 | 0.0 | Beat Offside Trap, Power Header | 61 | 89 | 86 | 72 | 83 | 80 | 73 | 46 | 63 | 82 | 76 | 85 | 81 | 84 | 46 | 82 | 85 | 80 | 88 | 70 | 83 | 47 | 86 | 72 | 70 | 27 | 47 | 33 | 15 | 5 | 12 | 14 | 14 | 84 | 84 | 84 | 79 | 82 | 82 | 82 | 79 | 79 | 79 | 79 | 78 | 74 | 74 | 74 | 78 | 63 | 63 | 63 | 63 | 63 | 61 | 61 | 61 | 61 | 61 |
| 194765 | A. Griezmann | 24 | 1991-03-21 | 176 | 67 | France | Atlético Madrid | 83 | 89 | 33000000 | ST, CAM, LM | Left | 3 | 3 | 4 | High/Medium | Lean | Yes | LS | 7.0 | 2014-07-29 | 2020.0 | 85.0 | 81.0 | 77.0 | 84.0 | 29.0 | 68.0 | 0.0 | Speed Dribbler (CPU AI Only) | 82 | 85 | 80 | 77 | 83 | 86 | 84 | 70 | 74 | 83 | 87 | 83 | 89 | 82 | 70 | 81 | 79 | 75 | 62 | 82 | 68 | 35 | 88 | 74 | 64 | 23 | 22 | -3 | 14 | 8 | 14 | 13 | 14 | 83 | 83 | 83 | 84 | 83 | 83 | 83 | 84 | 82 | 82 | 82 | 83 | 75 | 75 | 75 | 83 | 60 | 57 | 57 | 57 | 60 | 55 | 48 | 48 | 48 | 55 |
| 189509 | Thiago | 24 | 1991-04-11 | 174 | 70 | Spain | FC Bayern München | 83 | 87 | 30000000 | CM, CAM, CDM | Right | 3 | 3 | 4 | Medium/Medium | Normal | Yes | LDM | 6.0 | 2013-07-14 | 2019.0 | 72.0 | 75.0 | 81.0 | 89.0 | 57.0 | 64.0 | 0.0 | Injury Prone, Flair, Playmaker (CPU AI Only) | 69 | 69 | 52 | 89 | 95 | 92 | 84 | 77 | 83 | 88 | 77 | 68 | 90 | 82 | 83 | 85 | 72 | 72 | 60 | 86 | 67 | 67 | 83 | 84 | 75 | 49 | 58 | 57 | 6 | 11 | 7 | 9 | 13 | 77 | 77 | 77 | 82 | 82 | 82 | 82 | 82 | 85 | 85 | 85 | 81 | 83 | 83 | 83 | 81 | 71 | 73 | 73 | 73 | 71 | 68 | 64 | 64 | 64 | 68 |
| 193747 | Koke | 23 | 1992-01-08 | 178 | 74 | Spain | Atlético Madrid | 83 | 87 | 31000000 | CAM, LM, RM, CM | Right | 3 | 4 | 3 | High/High | Normal | Yes | LM | 6.0 | 2011-01-01 | 2019.0 | 74.0 | 72.0 | 85.0 | 81.0 | 53.0 | 73.0 | 0.0 | Playmaker (CPU AI Only) | 84 | 68 | 62 | 84 | 56 | 79 | 85 | 73 | 87 | 86 | 75 | 72 | 78 | 84 | 79 | 81 | 61 | 78 | 72 | 82 | 68 | 48 | 76 | 88 | 59 | 50 | 51 | 48 | 14 | 12 | 5 | 10 | 13 | 76 | 76 | 76 | 81 | 81 | 81 | 81 | 81 | 83 | 83 | 83 | 82 | 82 | 82 | 82 | 82 | 72 | 72 | 72 | 72 | 72 | 68 | 62 | 62 | 62 | 68 |
| 212218 | A. Laporte | 21 | 1994-05-27 | 189 | 85 | France | Athletic Club de Bilbao | 83 | 87 | 26000000 | CB | Left | 2 | 2 | 2 | Medium/Medium | Lean | Yes | LCB | 4.0 | 2012-10-01 | 2019.0 | 73.0 | 37.0 | 59.0 | 60.0 | 86.0 | 78.0 | 0.0 | NaN | 33 | 27 | 88 | 72 | 32 | 67 | 54 | 76 | 80 | 71 | 77 | 79 | 40 | 84 | 54 | 72 | 70 | 70 | 83 | 29 | 65 | 86 | 36 | 50 | 37 | 92 | 94 | 91 | 10 | 11 | 5 | 14 | 5 | 58 | 58 | 58 | 54 | 57 | 57 | 57 | 54 | 57 | 57 | 57 | 59 | 65 | 65 | 65 | 59 | 72 | 78 | 78 | 78 | 72 | 76 | 83 | 83 | 83 | 76 |
| 171833 | D. Sturridge | 25 | 1989-09-01 | 188 | 76 | England | Liverpool | 83 | 86 | 29500000 | ST, RW | Left | 3 | 2 | 3 | Medium/Low | Normal | Yes | RS | 15.0 | 2013-01-02 | 2019.0 | 89.0 | 83.0 | 69.0 | 81.0 | 25.0 | 70.0 | 0.0 | Injury Prone, Selfish, Finesse Shot, Flair, Sp... | 62 | 85 | 73 | 75 | 76 | 84 | 62 | 68 | 62 | 81 | 88 | 88 | 78 | 82 | 64 | 83 | 74 | 72 | 73 | 83 | 60 | 22 | 84 | 74 | 75 | 5 | 24 | -1 | 7 | 15 | 9 | 5 | 13 | 83 | 83 | 83 | 82 | 83 | 83 | 83 | 82 | 81 | 81 | 81 | 80 | 72 | 72 | 72 | 80 | 55 | 53 | 53 | 53 | 55 | 52 | 45 | 45 | 45 | 52 |
| 176676 | Marcelo | 27 | 1988-05-12 | 174 | 75 | Brazil | Real Madrid | 83 | 86 | 22500000 | LB | Left | 3 | 4 | 3 | High/Medium | Normal | Yes | LB | 12.0 | 2007-01-01 | 2020.0 | 81.0 | 68.0 | 77.0 | 84.0 | 81.0 | 79.0 | 0.0 | NaN | 85 | 68 | 75 | 78 | 54 | 83 | 80 | 67 | 76 | 86 | 77 | 83 | 79 | 85 | 84 | 83 | 75 | 80 | 75 | 65 | 86 | 83 | 67 | 70 | 59 | 84 | 87 | 89 | 12 | 5 | 5 | 5 | 9 | 76 | 76 | 76 | 79 | 78 | 78 | 78 | 79 | 78 | 78 | 78 | 80 | 79 | 79 | 79 | 80 | 84 | 82 | 82 | 82 | 84 | 83 | 82 | 82 | 82 | 83 |
| 202857 | K. Bellarabi | 25 | 1990-04-08 | 183 | 80 | Germany | Bayer 04 Leverkusen | 83 | 85 | 27500000 | RM, RW, CF | Right | 3 | 3 | 4 | High/Low | Lean | No | RM | 38.0 | 2011-07-01 | 2020.0 | 90.0 | 76.0 | 76.0 | 85.0 | 33.0 | 64.0 | 0.0 | Selfish, Flair, Technical Dribbler (CPU AI Only) | 81 | 77 | 48 | 81 | 74 | 86 | 70 | 58 | 67 | 87 | 91 | 90 | 87 | 81 | 79 | 82 | 75 | 82 | 60 | 79 | 49 | 36 | 83 | 81 | 57 | 28 | 30 | 33 | 10 | 12 | 7 | 11 | 10 | 78 | 78 | 78 | 84 | 82 | 82 | 82 | 84 | 82 | 82 | 82 | 83 | 75 | 75 | 75 | 83 | 64 | 58 | 58 | 58 | 64 | 59 | 48 | 48 | 48 | 59 |
| 143745 | A. Turan | 28 | 1987-01-30 | 177 | 76 | Turkey | FC Barcelona | 83 | 84 | 25000000 | RM, LM | Right | 3 | 4 | 4 | High/High | Normal | Yes | SUB | 16.0 | 2015-07-06 | 2020.0 | 75.0 | 74.0 | 82.0 | 87.0 | 61.0 | 73.0 | 0.0 | Finesse Shot, Flair, Playmaker (CPU AI Only), ... | 84 | 72 | 70 | 82 | 80 | 87 | 81 | 64 | 80 | 90 | 79 | 71 | 86 | 79 | 59 | 74 | 79 | 76 | 69 | 77 | 78 | 58 | 82 | 86 | 73 | 54 | 62 | 66 | 11 | 7 | 11 | 12 | 11 | 79 | 79 | 79 | 83 | 83 | 83 | 83 | 83 | 84 | 84 | 84 | 83 | 82 | 82 | 82 | 83 | 75 | 74 | 74 | 74 | 75 | 73 | 69 | 69 | 69 | 73 |
| 167628 | S. Ruffier | 28 | 1986-09-27 | 188 | 90 | France | AS Saint-Étienne | 83 | 84 | 22500000 | GK | Right | 3 | 3 | 1 | Medium/Medium | Normal | No | GK | 16.0 | 2011-07-01 | 2018.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 54.0 | NaN | -3 | -3 | -1 | 33 | 1 | -1 | 9 | -1 | 34 | 4 | 55 | 54 | 54 | 79 | 55 | 21 | 72 | 37 | 76 | -3 | 31 | 25 | -1 | 73 | 25 | -3 | -1 | -3 | 84 | 81 | 77 | 83 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 170481 | E. Garay | 28 | 1986-10-10 | 188 | 80 | Argentina | Zenit St. Petersburg | 83 | 84 | 21000000 | CB | Right | 3 | 3 | 2 | Medium/Medium | Normal | No | RCB | 24.0 | 2014-07-01 | 2019.0 | 52.0 | 57.0 | 68.0 | 64.0 | 87.0 | 78.0 | 0.0 | NaN | 62 | 48 | 86 | 72 | 46 | 62 | 55 | 74 | 75 | 73 | 50 | 54 | 57 | 77 | 38 | 76 | 71 | 70 | 85 | 60 | 74 | 92 | 49 | 63 | 66 | 88 | 87 | 84 | 14 | 6 | 13 | 13 | 14 | 65 | 65 | 65 | 62 | 64 | 64 | 64 | 62 | 65 | 65 | 65 | 65 | 71 | 71 | 71 | 65 | 75 | 80 | 80 | 80 | 75 | 77 | 83 | 83 | 83 | 77 |
| 179784 | B. Höwedes | 27 | 1988-02-29 | 187 | 82 | Germany | FC Schalke 04 | 83 | 84 | 21500000 | CB, RB, LB | Right | 3 | 3 | 2 | Medium/Medium | Normal | Yes | SUB | 4.0 | 2007-07-01 | 2017.0 | 65.0 | 47.0 | 63.0 | 65.0 | 85.0 | 78.0 | 0.0 | Injury Prone, Leadership, Team Player | 51 | 48 | 85 | 74 | 38 | 60 | 38 | 52 | 64 | 68 | 62 | 66 | 65 | 82 | 62 | 62 | 82 | 69 | 81 | 31 | 78 | 88 | 51 | 57 | 56 | 87 | 85 | 84 | 14 | 6 | 9 | 10 | 7 | 64 | 64 | 64 | 62 | 63 | 63 | 63 | 62 | 63 | 63 | 63 | 65 | 68 | 68 | 68 | 65 | 75 | 78 | 78 | 78 | 75 | 78 | 83 | 83 | 83 | 78 |
| 179944 | David Luiz | 28 | 1987-04-22 | 189 | 84 | Brazil | Paris Saint-Germain | 83 | 84 | 21000000 | CB | Right | 4 | 3 | 3 | High/Medium | Normal | Yes | LCB | 32.0 | 2014-07-01 | 2019.0 | 73.0 | 64.0 | 73.0 | 73.0 | 83.0 | 77.0 | 0.0 | Long Throw-in, Power Free-Kick, Long Passer (C... | 68 | 56 | 83 | 78 | 67 | 70 | 70 | 76 | 76 | 79 | 69 | 68 | 73 | 81 | 56 | 74 | 84 | 67 | 77 | 71 | 84 | 80 | 57 | 68 | 75 | 82 | 88 | 86 | 11 | 12 | 10 | 7 | 14 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 73 | 73 | 73 | 73 | 76 | 76 | 76 | 73 | 79 | 81 | 81 | 81 | 79 | 80 | 83 | 83 | 83 | 80 |
| 184144 | N. Gaitán | 27 | 1988-02-23 | 174 | 69 | Argentina | SL Benfica | 83 | 84 | 25500000 | LM | Left | 2 | 2 | 4 | Medium/Medium | Normal | No | LM | 10.0 | 2010-07-01 | 2018.0 | 83.0 | 74.0 | 84.0 | 86.0 | 40.0 | 57.0 | 0.0 | Avoids Using Weaker Foot, Flair, Playmaker (CP... | 86 | 73 | 57 | 86 | 84 | 87 | 89 | 72 | 81 | 85 | 85 | 82 | 88 | 83 | 83 | 75 | 76 | 70 | 50 | 74 | 57 | 40 | 79 | 85 | 71 | 30 | 42 | 43 | 8 | 11 | 9 | 7 | 5 | 76 | 76 | 76 | 83 | 81 | 81 | 81 | 83 | 83 | 83 | 83 | 83 | 78 | 78 | 78 | 83 | 66 | 62 | 62 | 62 | 66 | 62 | 51 | 51 | 51 | 62 |
| 53302 | D. De Rossi | 31 | 1983-07-24 | 185 | 83 | Italy | Roma | 83 | 83 | 15000000 | CDM | Right | 3 | 4 | 2 | Medium/High | Normal | Yes | CDM | 16.0 | 2002-07-01 | 2017.0 | 68.0 | 66.0 | 74.0 | 72.0 | 83.0 | 84.0 | 0.0 | Leadership, One Club Player, Team Player | 60 | 53 | 84 | 82 | 74 | 69 | 67 | 71 | 79 | 78 | 69 | 68 | 69 | 80 | 75 | 81 | 78 | 84 | 84 | 75 | 84 | 88 | 59 | 72 | 75 | 75 | 85 | 83 | 10 | 7 | 13 | 10 | 9 | 72 | 72 | 72 | 70 | 72 | 72 | 72 | 70 | 73 | 73 | 73 | 72 | 78 | 78 | 78 | 72 | 79 | 83 | 83 | 83 | 79 | 80 | 83 | 83 | 83 | 80 |
| 53612 | P. Mertesacker | 30 | 1984-09-29 | 198 | 90 | Germany | Arsenal | 83 | 83 | 17000000 | CB | Right | 3 | 3 | 2 | Medium/Medium | Normal | Yes | SUB | 4.0 | 2011-08-31 | 2017.0 | 31.0 | 41.0 | 57.0 | 48.0 | 88.0 | 75.0 | 0.0 | Injury Free, Leadership, Power Header | 38 | 36 | 86 | 71 | 30 | 39 | 39 | 45 | 58 | 65 | 29 | 33 | 29 | 84 | 28 | 71 | 33 | 64 | 88 | 25 | 69 | 90 | 43 | 58 | 42 | 89 | 89 | 88 | 12 | 13 | 5 | 12 | 8 | 57 | 57 | 57 | 49 | 54 | 54 | 54 | 49 | 54 | 54 | 54 | 53 | 64 | 64 | 64 | 53 | 69 | 77 | 77 | 77 | 69 | 72 | 83 | 83 | 83 | 72 |
| 112316 | J. Mathieu | 31 | 1983-10-29 | 189 | 84 | France | FC Barcelona | 83 | 83 | 15000000 | CB, LB | Left | 3 | 2 | 2 | High/Medium | Normal | Yes | SUB | 24.0 | 2014-07-23 | 2018.0 | 76.0 | 64.0 | 66.0 | 55.0 | 84.0 | 82.0 | 0.0 | Injury Prone, Avoids Using Weaker Foot, Dives ... | 79 | 52 | 83 | 66 | 68 | 45 | 79 | 70 | 76 | 65 | 70 | 80 | 65 | 76 | 50 | 80 | 75 | 72 | 90 | 77 | 81 | 85 | 48 | 42 | 60 | 85 | 88 | 79 | 13 | 14 | 10 | 9 | 5 | 67 | 67 | 67 | 63 | 62 | 62 | 62 | 63 | 61 | 61 | 61 | 64 | 66 | 66 | 66 | 64 | 76 | 77 | 77 | 77 | 76 | 79 | 83 | 83 | 83 | 79 |
| 142754 | J. Mascherano | 31 | 1984-06-08 | 174 | 73 | Argentina | FC Barcelona | 83 | 83 | 15000000 | CB, CDM | Right | 3 | 3 | 2 | Medium/High | Normal | Yes | LCB | 14.0 | 2010-08-01 | 2018.0 | 68.0 | 59.0 | 71.0 | 69.0 | 85.0 | 80.0 | 0.0 | Dives Into Tackles (CPU AI Only), Long Passer ... | 63 | 51 | 70 | 80 | 59 | 58 | 63 | 50 | 75 | 82 | 69 | 67 | 72 | 82 | 79 | 76 | 75 | 87 | 74 | 59 | 89 | 92 | 51 | 68 | 64 | 86 | 87 | 85 | 6 | 10 | 8 | 5 | 5 | 67 | 67 | 67 | 68 | 68 | 68 | 68 | 68 | 70 | 70 | 70 | 70 | 76 | 76 | 76 | 70 | 81 | 84 | 84 | 84 | 81 | 82 | 83 | 83 | 83 | 82 |
| 162347 | João Moutinho | 28 | 1986-09-08 | 170 | 61 | Portugal | AS Monaco | 83 | 83 | 23000000 | CAM, CDM | Right | 3 | 4 | 3 | Medium/Medium | Normal | No | CAM | 8.0 | 2013-07-01 | 2018.0 | 71.0 | 76.0 | 83.0 | 82.0 | 71.0 | 71.0 | 0.0 | Injury Free, Playmaker (CPU AI Only) | 78 | 74 | 68 | 86 | 77 | 81 | 82 | 79 | 83 | 84 | 71 | 65 | 80 | 86 | 83 | 78 | 81 | 91 | 56 | 83 | 77 | 75 | 78 | 84 | 70 | 67 | 75 | 68 | 13 | 15 | 15 | 13 | 13 | 77 | 77 | 77 | 81 | 81 | 81 | 81 | 81 | 83 | 83 | 83 | 82 | 84 | 84 | 84 | 82 | 79 | 80 | 80 | 80 | 79 | 77 | 73 | 73 | 73 | 77 |
| 163631 | L. Baines | 30 | 1984-12-11 | 170 | 70 | England | Everton | 83 | 83 | 16500000 | LB | Left | 3 | 3 | 3 | High/Medium | Normal | Yes | SUB | 3.0 | 2007-08-07 | 2018.0 | 75.0 | 75.0 | 81.0 | 77.0 | 82.0 | 74.0 | 0.0 | Early Crosser | 94 | 70 | 74 | 78 | 63 | 77 | 82 | 82 | 74 | 75 | 77 | 73 | 76 | 80 | 85 | 84 | 72 | 87 | 70 | 75 | 74 | 80 | 76 | 77 | 92 | 86 | 86 | 87 | 8 | 15 | 10 | 13 | 12 | 76 | 76 | 76 | 78 | 77 | 77 | 77 | 78 | 78 | 78 | 78 | 79 | 79 | 79 | 79 | 79 | 83 | 81 | 81 | 81 | 83 | 83 | 80 | 80 | 80 | 83 |
| 163705 | S. Mandanda | 30 | 1985-03-28 | 185 | 82 | France | Olympique de Marseille | 83 | 83 | 19000000 | GK | Right | 3 | 3 | 1 | Medium/Medium | Normal | Yes | GK | 30.0 | 2007-07-01 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 48.0 | Leadership | 3 | 1 | 1 | 31 | 1 | -3 | 1 | -1 | 28 | -1 | 53 | 43 | 52 | 83 | 36 | 14 | 74 | 31 | 61 | -3 | 37 | 23 | -3 | 3 | -3 | -5 | -1 | 1 | 87 | 78 | 79 | 82 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 164468 | G. Cahill | 29 | 1985-12-19 | 193 | 86 | England | Chelsea | 83 | 83 | 17500000 | CB | Right | 2 | 3 | 2 | Medium/Medium | Normal | Yes | LCB | 24.0 | 2012-01-16 | 2017.0 | 73.0 | 58.0 | 52.0 | 62.0 | 86.0 | 76.0 | 0.0 | Power Header | 28 | 56 | 86 | 65 | 47 | 58 | 48 | 27 | 61 | 64 | 69 | 76 | 67 | 84 | 51 | 63 | 83 | 68 | 79 | 66 | 78 | 90 | 45 | 56 | 53 | 86 | 89 | 90 | 5 | 12 | 9 | 5 | 12 | 65 | 65 | 65 | 60 | 62 | 62 | 62 | 60 | 62 | 62 | 62 | 60 | 65 | 65 | 65 | 60 | 71 | 75 | 75 | 75 | 71 | 75 | 83 | 83 | 83 | 75 |
| 165229 | L. Koscielny | 29 | 1985-09-10 | 186 | 75 | France | Arsenal | 83 | 83 | 17500000 | CB | Right | 3 | 3 | 2 | Medium/High | Lean | Yes | LCB | 6.0 | 2010-07-07 | 2019.0 | 78.0 | 40.0 | 62.0 | 65.0 | 84.0 | 75.0 | 0.0 | Power Header | 54 | 32 | 81 | 75 | 35 | 62 | 22 | 49 | 67 | 67 | 79 | 74 | 70 | 78 | 62 | 54 | 86 | 73 | 71 | 47 | 88 | 85 | 31 | 56 | 51 | 85 | 89 | 87 | 13 | 11 | 9 | 11 | 7 | 58 | 58 | 58 | 61 | 60 | 60 | 60 | 61 | 62 | 62 | 62 | 64 | 67 | 67 | 67 | 64 | 76 | 78 | 78 | 78 | 76 | 79 | 83 | 83 | 83 | 79 |
| 165239 | S. Nasri | 28 | 1987-06-26 | 175 | 75 | France | Manchester City | 83 | 83 | 22500000 | LM, RM | Right | 3 | 4 | 4 | High/Medium | Normal | Yes | SUB | 8.0 | 2011-08-24 | 2019.0 | 81.0 | 76.0 | 83.0 | 86.0 | 38.0 | 58.0 | 0.0 | Flair, Technical Dribbler (CPU AI Only) | 81 | 76 | 29 | 86 | 73 | 86 | 78 | 79 | 72 | 88 | 86 | 79 | 82 | 77 | 92 | 75 | 50 | 72 | 60 | 77 | 33 | 37 | 81 | 85 | 73 | 35 | 37 | 44 | -3 | -7 | -11 | -13 | -6 | 77 | 77 | 77 | 84 | 83 | 83 | 83 | 84 | 84 | 84 | 84 | 83 | 79 | 79 | 79 | 83 | 66 | 62 | 62 | 62 | 66 | 61 | 49 | 49 | 49 | 61 |
| 165580 | Diego Alves | 30 | 1985-06-24 | 188 | 83 | Brazil | Valencia CF | 83 | 83 | 19000000 | GK | Left | 3 | 2 | 1 | Medium/Medium | Normal | Yes | RES | 1.0 | 2011-07-01 | 2019.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 54.0 | NaN | 14 | 20 | 20 | 33 | 14 | 3 | 16 | 21 | 33 | 5 | 55 | 54 | 69 | 80 | 57 | 3 | 84 | 43 | 67 | 12 | 44 | 13 | 1 | 103 | 18 | 3 | 1 | 5 | 88 | 76 | 76 | 77 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 167397 | Falcao | 29 | 1986-02-10 | 177 | 72 | Colombia | Chelsea | 83 | 83 | 23000000 | ST | Right | 4 | 4 | 4 | High/Medium | Normal | Yes | SUB | 9.0 | 2015-07-01 | 2016.0 | 72.0 | 82.0 | 64.0 | 79.0 | 37.0 | 73.0 | 0.0 | Injury Prone, Finesse Shot, Power Header | 55 | 84 | 90 | 69 | 81 | 72 | 83 | 71 | 53 | 83 | 73 | 66 | 85 | 85 | 75 | 79 | 92 | 71 | 73 | 77 | 70 | 41 | 85 | 68 | 81 | 7 | 42 | 13 | 10 | 13 | 6 | 9 | 5 | 83 | 83 | 83 | 78 | 81 | 81 | 81 | 78 | 78 | 78 | 78 | 75 | 72 | 72 | 72 | 75 | 58 | 58 | 58 | 58 | 58 | 56 | 55 | 55 | 55 | 56 |
| 173221 | A. Candreva | 28 | 1987-02-28 | 181 | 72 | Italy | Lazio | 83 | 83 | 23000000 | RW | Right | 2 | 4 | 3 | High/Medium | Lean | No | RAM | 87.0 | 2012-01-01 | 2019.0 | 86.0 | 81.0 | 82.0 | 85.0 | 45.0 | 74.0 | 0.0 | Power Free-Kick, Flair, Long Shot Taker (CPU A... | 90 | 78 | 55 | 80 | 79 | 87 | 87 | 79 | 75 | 89 | 88 | 86 | 80 | 85 | 76 | 91 | 57 | 93 | 68 | 88 | 66 | 49 | 77 | 81 | 88 | 41 | 44 | 44 | 10 | 4 | 14 | 4 | 14 | 79 | 79 | 79 | 83 | 82 | 82 | 82 | 83 | 82 | 82 | 82 | 84 | 79 | 79 | 79 | 84 | 71 | 66 | 66 | 66 | 71 | 67 | 56 | 56 | 56 | 67 |
| 174543 | C. Bravo | 32 | 1983-04-13 | 184 | 80 | Chile | FC Barcelona | 83 | 83 | 17500000 | GK | Right | 3 | 3 | 1 | Medium/Medium | Normal | No | GK | 13.0 | 2014-07-01 | 2018.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 56.0 | Puncher | -1 | 1 | 11 | 32 | -3 | 1 | 24 | 95 | 31 | 5 | 58 | 54 | 64 | 77 | 64 | 12 | 81 | 44 | 70 | 11 | 40 | 23 | 5 | 101 | 23 | -3 | 11 | 13 | 81 | 82 | 89 | 78 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 179846 | S. Khedira | 28 | 1987-04-04 | 189 | 85 | Germany | Juventus | 83 | 83 | 18500000 | CDM, CM | Right | 3 | 4 | 2 | Medium/High | Normal | Yes | SUB | 6.0 | 2015-07-01 | 2019.0 | 62.0 | 70.0 | 73.0 | 70.0 | 83.0 | 86.0 | 0.0 | Injury Prone | 55 | 62 | 72 | 81 | 78 | 76 | 69 | 58 | 75 | 77 | 60 | 75 | 58 | 85 | 51 | 84 | 68 | 82 | 89 | 76 | 88 | 85 | 80 | 77 | 61 | 83 | 86 | 79 | 11 | 9 | 5 | 15 | 8 | 75 | 75 | 75 | 71 | 75 | 75 | 75 | 71 | 75 | 75 | 75 | 73 | 80 | 80 | 80 | 73 | 78 | 83 | 83 | 83 | 78 | 79 | 83 | 83 | 83 | 79 |
| 184344 | L. Bonucci | 28 | 1987-05-01 | 190 | 82 | Italy | Juventus | 83 | 83 | 18500000 | CB | Right | 2 | 3 | 2 | Medium/High | Lean | Yes | RCB | 19.0 | 2010-07-01 | 2020.0 | 71.0 | 50.0 | 66.0 | 67.0 | 85.0 | 81.0 | 0.0 | Power Free-Kick, Injury Free, Leadership, Long... | 58 | 41 | 84 | 75 | 65 | 63 | 60 | 81 | 75 | 73 | 61 | 77 | 69 | 86 | 57 | 73 | 83 | 75 | 85 | 73 | 89 | 85 | 40 | 63 | 76 | 88 | 93 | 90 | 2 | 2 | 3 | 2 | 4 | 63 | 63 | 63 | 62 | 63 | 63 | 63 | 62 | 65 | 65 | 65 | 64 | 71 | 71 | 71 | 64 | 75 | 80 | 80 | 80 | 75 | 77 | 83 | 83 | 83 | 77 |
| 185221 | Luiz Gustavo | 27 | 1987-07-23 | 187 | 80 | Brazil | VfL Wolfsburg | 83 | 83 | 19000000 | CDM | Left | 3 | 3 | 2 | Medium/High | Lean | Yes | CDM | 22.0 | 2013-08-16 | 2018.0 | 70.0 | 60.0 | 70.0 | 71.0 | 84.0 | 82.0 | 0.0 | Dives Into Tackles (CPU AI Only) | 59 | 47 | 76 | 81 | 50 | 68 | 36 | 39 | 79 | 75 | 69 | 67 | 64 | 83 | 59 | 87 | 76 | 81 | 81 | 75 | 88 | 85 | 51 | 70 | 60 | 81 | 87 | 88 | 10 | 6 | 11 | 12 | 13 | 68 | 68 | 68 | 69 | 69 | 69 | 69 | 69 | 71 | 71 | 71 | 71 | 76 | 76 | 76 | 71 | 80 | 83 | 83 | 83 | 80 | 81 | 83 | 83 | 83 | 81 |
| 189505 | Pedro | 27 | 1987-07-28 | 167 | 62 | Spain | Chelsea | 83 | 83 | 24000000 | LW, RW | Right | 3 | 5 | 4 | High/Medium | Normal | Yes | RM | 17.0 | 2015-08-20 | 2019.0 | 82.0 | 76.0 | 77.0 | 84.0 | 37.0 | 62.0 | 0.0 | NaN | 78 | 80 | 55 | 83 | 70 | 84 | 81 | 57 | 74 | 85 | 86 | 78 | 84 | 85 | 82 | 71 | 67 | 78 | 58 | 73 | 56 | 49 | 84 | 74 | 66 | 30 | 32 | 31 | 5 | 11 | 12 | 15 | 9 | 78 | 78 | 78 | 83 | 82 | 82 | 82 | 83 | 82 | 82 | 82 | 83 | 77 | 77 | 77 | 83 | 66 | 62 | 62 | 62 | 66 | 61 | 50 | 50 | 50 | 61 |
| 201535 | R. Varane | 22 | 1993-04-25 | 191 | 78 | France | Real Madrid | 82 | 89 | 24500000 | CB | Right | 3 | 3 | 2 | Medium/Medium | Lean | Yes | SUB | 2.0 | 2011-07-01 | 2020.0 | 79.0 | 45.0 | 60.0 | 63.0 | 84.0 | 77.0 | 0.0 | NaN | 36 | 40 | 82 | 71 | 42 | 57 | 46 | 54 | 74 | 74 | 77 | 79 | 56 | 78 | 47 | 52 | 72 | 68 | 84 | 53 | 73 | 85 | 44 | 56 | 43 | 86 | 86 | 85 | 11 | 11 | 9 | 5 | 14 | 62 | 62 | 62 | 61 | 63 | 63 | 63 | 61 | 64 | 64 | 64 | 64 | 69 | 69 | 69 | 64 | 74 | 79 | 79 | 79 | 74 | 77 | 82 | 82 | 82 | 77 |
| 192448 | M. ter Stegen | 23 | 1992-04-30 | 187 | 85 | Germany | FC Barcelona | 82 | 88 | 23500000 | GK | Right | 2 | 4 | 1 | Medium/Medium | Normal | Yes | SUB | 1.0 | 2014-07-01 | 2019.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.0 | NaN | 5 | 3 | -5 | 30 | 3 | 9 | 11 | -1 | 38 | 8 | 38 | 50 | 37 | 79 | 43 | 6 | 82 | 35 | 79 | -5 | 43 | 22 | -3 | 89 | 17 | -5 | 1 | -5 | 85 | 79 | 82 | 78 | 85 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 230133 | F. Moncur | 18 | 1996-09-08 | 175 | 68 | England | Leyton Orient | 49 | 60 | 50000 | CM | Right | 1 | 3 | 3 | Medium/Medium | Normal | No | SUB | 23.0 | 2015-07-08 | 2016.0 | 68.0 | 42.0 | 53.0 | 50.0 | 36.0 | 45.0 | 0.0 | NaN | 41 | 42 | 37 | 62 | 36 | 48 | 41 | 42 | 53 | 48 | 69 | 67 | 56 | 47 | 76 | 45 | 52 | 48 | 44 | 39 | 41 | 23 | 41 | 53 | 40 | 37 | 40 | 49 | 11 | 7 | 6 | 8 | 6 | 46 | 46 | 46 | 50 | 49 | 49 | 49 | 50 | 51 | 51 | 51 | 51 | 49 | 49 | 49 | 51 | 46 | 45 | 45 | 45 | 46 | 46 | 41 | 41 | 41 | 46 |
| 222007 | C. O'Malley | 20 | 1994-08-01 | 182 | 74 | Republic of Ireland | St. Patrick's Athletic | 49 | 59 | 50000 | GK | Right | 1 | 2 | 1 | Medium/Medium | Lean | No | SUB | 16.0 | 2015-01-01 | 2015.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 40.0 | NaN | 22 | 5 | 1 | 22 | 13 | 1 | 5 | 11 | 13 | 9 | 38 | 41 | 45 | 39 | 51 | 13 | 52 | 33 | 40 | -1 | 11 | 27 | 13 | 1 | 25 | 13 | -1 | 7 | 54 | 48 | 42 | 47 | 57 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 225866 | M. Agyemang | 18 | 1996-11-22 | 175 | 67 | England | Leyton Orient | 49 | 59 | 40000 | RM, RWB | Right | 1 | 3 | 3 | Medium/Medium | Normal | No | RES | 24.0 | 2014-10-21 | 2016.0 | 71.0 | 44.0 | 44.0 | 50.0 | 25.0 | 43.0 | 0.0 | NaN | 45 | 41 | 37 | 49 | 43 | 48 | 39 | 33 | 38 | 50 | 68 | 74 | 57 | 32 | 76 | 53 | 42 | 51 | 43 | 39 | 35 | 20 | 49 | 42 | 45 | 28 | 22 | 26 | 10 | 6 | 12 | 15 | 15 | 47 | 47 | 47 | 49 | 48 | 48 | 48 | 49 | 47 | 47 | 47 | 49 | 42 | 42 | 42 | 49 | 40 | 35 | 35 | 35 | 40 | 38 | 32 | 32 | 32 | 38 |
| 227929 | R. Wintle | 17 | 1997-12-17 | 165 | 64 | England | Crewe Alexandra | 49 | 59 | 50000 | ST | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 21.0 | 2015-02-02 | 2016.0 | 62.0 | 47.0 | 46.0 | 57.0 | 35.0 | 41.0 | 0.0 | NaN | 44 | 53 | 36 | 49 | 37 | 55 | 39 | 43 | 45 | 55 | 61 | 62 | 64 | 45 | 90 | 51 | 56 | 53 | 34 | 32 | 42 | 23 | 51 | 47 | 43 | 32 | 41 | 44 | 7 | 16 | 8 | 13 | 10 | 49 | 49 | 49 | 52 | 51 | 51 | 51 | 52 | 51 | 51 | 51 | 52 | 47 | 47 | 47 | 52 | 45 | 42 | 42 | 42 | 45 | 44 | 39 | 39 | 39 | 44 |
| 228888 | G. Edmundson | 17 | 1997-08-15 | 185 | 75 | England | Oldham Athletic | 49 | 59 | 40000 | CB | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 27.0 | 2015-05-14 | 2016.0 | 60.0 | 25.0 | 31.0 | 34.0 | 49.0 | 56.0 | 0.0 | NaN | 29 | 20 | 47 | 36 | 25 | 27 | 26 | 29 | 30 | 33 | 62 | 59 | 55 | 47 | 58 | 36 | 70 | 57 | 59 | 19 | 44 | 47 | 29 | 26 | 39 | 43 | 54 | 54 | 14 | 9 | 8 | 14 | 15 | 36 | 36 | 36 | 35 | 34 | 34 | 34 | 35 | 33 | 33 | 33 | 36 | 35 | 35 | 35 | 36 | 45 | 43 | 43 | 43 | 45 | 47 | 49 | 49 | 49 | 47 |
| 225867 | V. Adeboyejo | 17 | 1998-01-12 | 178 | 63 | England | Leyton Orient | 49 | 58 | 40000 | ST | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 29.0 | 2014-10-21 | 2016.0 | 71.0 | 51.0 | 41.0 | 48.0 | 22.0 | 40.0 | 0.0 | NaN | 33 | 55 | 50 | 45 | 34 | 45 | 32 | 34 | 34 | 42 | 68 | 73 | 64 | 50 | 81 | 47 | 52 | 56 | 34 | 47 | 34 | 15 | 47 | 50 | 58 | 20 | 19 | 18 | 12 | 9 | 13 | 11 | 10 | 49 | 49 | 49 | 49 | 50 | 50 | 50 | 49 | 48 | 48 | 48 | 48 | 42 | 42 | 42 | 48 | 37 | 33 | 33 | 33 | 37 | 36 | 30 | 30 | 30 | 36 |
| 228331 | J. Jewson | 17 | 1998-03-03 | 178 | 78 | England | Hartlepool United | 49 | 58 | 40000 | ST, CAM, CM | Right | 1 | 3 | 3 | High/Medium | Lean | No | RES | 35.0 | 2014-12-15 | 2016.0 | 63.0 | 49.0 | 41.0 | 56.0 | 18.0 | 52.0 | 0.0 | NaN | 35 | 47 | 42 | 42 | 47 | 60 | 48 | 29 | 32 | 51 | 65 | 61 | 57 | 52 | 66 | 62 | 52 | 58 | 59 | 47 | 27 | 7 | 38 | 56 | 60 | 1 | 9 | -1 | 16 | 9 | 11 | 13 | 17 | 49 | 49 | 49 | 50 | 50 | 50 | 50 | 50 | 49 | 49 | 49 | 49 | 43 | 43 | 43 | 49 | 36 | 33 | 33 | 33 | 36 | 34 | 29 | 29 | 29 | 34 |
| 229979 | R. Vergara | 20 | 1995-06-13 | 177 | 69 | Colombia | Boyacá Chicó FC | 49 | 57 | 50000 | ST | Right | 1 | 2 | 3 | Low/Low | Lean | No | RES | 24.0 | 2015-07-16 | 2021.0 | 68.0 | 49.0 | 36.0 | 47.0 | 18.0 | 55.0 | 0.0 | NaN | 26 | 48 | 48 | 41 | 33 | 44 | 33 | 28 | 31 | 42 | 69 | 67 | 71 | 54 | 70 | 52 | 64 | 77 | 72 | 50 | 26 | 7 | 43 | 45 | 63 | -3 | 7 | 11 | 6 | 5 | 11 | 6 | 11 | 49 | 49 | 49 | 47 | 48 | 48 | 48 | 47 | 46 | 46 | 46 | 46 | 41 | 41 | 41 | 46 | 36 | 32 | 32 | 32 | 36 | 35 | 30 | 30 | 30 | 35 |
| 230806 | M. Carberry | 17 | 1998-03-05 | 170 | 63 | England | Doncaster Rovers | 49 | 57 | 30000 | LB, LM | Left | 1 | 2 | 2 | High/Medium | Normal | No | RES | 33.0 | 2015-07-01 | 2018.0 | 61.0 | 26.0 | 37.0 | 43.0 | 46.0 | 43.0 | 0.0 | NaN | 45 | 20 | 34 | 36 | 28 | 40 | 36 | 27 | 34 | 37 | 63 | 60 | 54 | 53 | 75 | 32 | 50 | 57 | 36 | 27 | 40 | 47 | 54 | 34 | 29 | 40 | 54 | 52 | 11 | 6 | 8 | 8 | 13 | 38 | 38 | 38 | 42 | 41 | 41 | 41 | 42 | 40 | 40 | 40 | 43 | 40 | 40 | 40 | 43 | 48 | 43 | 43 | 43 | 48 | 49 | 45 | 45 | 45 | 49 |
| 224392 | T. McCready | 24 | 1991-06-07 | 185 | 71 | England | Exeter City | 49 | 56 | 50000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | SUB | 23.0 | 2015-02-02 | 2021.0 | 64.0 | 42.0 | 50.0 | 54.0 | 35.0 | 49.0 | 0.0 | NaN | 43 | 42 | 42 | 57 | 42 | 55 | 43 | 39 | 56 | 52 | 62 | 65 | 64 | 48 | 60 | 51 | 56 | 49 | 51 | 38 | 42 | 36 | 45 | 48 | 47 | 34 | 37 | 35 | 9 | 11 | 12 | 11 | 17 | 48 | 48 | 48 | 50 | 50 | 50 | 50 | 50 | 51 | 51 | 51 | 51 | 49 | 49 | 49 | 51 | 45 | 45 | 45 | 45 | 45 | 44 | 41 | 41 | 41 | 44 |
| 224502 | B. Whitehouse | 19 | 1996-06-13 | 180 | 72 | England | Doncaster Rovers | 49 | 56 | 40000 | RM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 32.0 | 2014-07-01 | 2017.0 | 62.0 | 45.0 | 46.0 | 52.0 | 25.0 | 49.0 | 0.0 | NaN | 48 | 46 | 32 | 55 | 45 | 59 | 45 | 33 | 47 | 47 | 60 | 68 | 58 | 34 | 73 | 54 | 49 | 50 | 62 | 35 | 31 | 24 | 48 | 45 | 54 | 22 | 31 | 31 | 11 | 13 | 12 | 9 | 7 | 46 | 46 | 46 | 48 | 47 | 47 | 47 | 48 | 47 | 47 | 47 | 49 | 43 | 43 | 43 | 49 | 40 | 37 | 37 | 37 | 40 | 38 | 33 | 33 | 33 | 38 |
| 226145 | N. Bondswell | 18 | 1997-02-10 | 173 | 64 | England | Morecambe | 49 | 56 | 40000 | LM, LB | Left | 1 | 3 | 3 | Medium/Medium | Lean | No | RES | 14.0 | 2014-11-20 | 2021.0 | 72.0 | 40.0 | 44.0 | 53.0 | 27.0 | 38.0 | 0.0 | NaN | 45 | 39 | 37 | 48 | 40 | 53 | 45 | 37 | 41 | 50 | 71 | 73 | 71 | 40 | 65 | 49 | 42 | 54 | 27 | 38 | 31 | 23 | 42 | 43 | 48 | 26 | 29 | 29 | 17 | 15 | 14 | 10 | 10 | 45 | 45 | 45 | 49 | 47 | 47 | 47 | 49 | 47 | 47 | 47 | 49 | 43 | 43 | 43 | 49 | 41 | 36 | 36 | 36 | 41 | 40 | 32 | 32 | 32 | 40 |
| 226821 | C. Brandt | 22 | 1992-09-15 | 185 | 75 | United States | New York City FC | 49 | 56 | 50000 | CM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 16.0 | 2015-01-15 | 2021.0 | 61.0 | 36.0 | 51.0 | 49.0 | 39.0 | 57.0 | 0.0 | NaN | 45 | 29 | 43 | 61 | 35 | 51 | 40 | 38 | 52 | 43 | 68 | 55 | 57 | 52 | 58 | 49 | 64 | 55 | 57 | 41 | 58 | 39 | 49 | 51 | 41 | 41 | 37 | 43 | 8 | 11 | 14 | 8 | 15 | 45 | 45 | 45 | 48 | 48 | 48 | 48 | 48 | 49 | 49 | 49 | 50 | 49 | 49 | 49 | 50 | 47 | 47 | 47 | 47 | 47 | 46 | 45 | 45 | 45 | 46 |
| 222742 | D. Thomas | 19 | 1995-11-23 | 185 | 70 | England | Bristol Rovers | 49 | 55 | 40000 | CM | Right | 1 | 3 | 2 | Medium/Low | Lean | No | RES | 18.0 | 2012-07-01 | 2021.0 | 77.0 | 42.0 | 49.0 | 58.0 | 34.0 | 49.0 | 0.0 | NaN | 39 | 39 | 37 | 57 | 40 | 56 | 47 | 36 | 51 | 55 | 78 | 76 | 75 | 47 | 70 | 53 | 55 | 55 | 47 | 36 | 45 | 29 | 47 | 46 | 47 | 34 | 35 | 41 | 15 | 15 | 9 | 11 | 12 | 49 | 49 | 49 | 53 | 52 | 52 | 52 | 53 | 53 | 53 | 53 | 54 | 49 | 49 | 49 | 54 | 47 | 45 | 45 | 45 | 47 | 46 | 41 | 41 | 41 | 46 |
| 225772 | K. Green | 18 | 1997-06-30 | 175 | 80 | England | Hartlepool United | 49 | 55 | 40000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 24.0 | 2014-10-01 | 2016.0 | 62.0 | 35.0 | 49.0 | 47.0 | 41.0 | 58.0 | 0.0 | NaN | 40 | 28 | 42 | 60 | 36 | 42 | 37 | 34 | 56 | 51 | 64 | 61 | 58 | 48 | 69 | 50 | 59 | 55 | 61 | 33 | 54 | 43 | 51 | 44 | 45 | 41 | 42 | 47 | 11 | 10 | 10 | 9 | 10 | 45 | 45 | 45 | 47 | 46 | 46 | 46 | 47 | 48 | 48 | 48 | 49 | 49 | 49 | 49 | 49 | 48 | 49 | 49 | 49 | 48 | 48 | 47 | 47 | 47 | 48 |
| 226053 | K. Steenson | 18 | 1996-11-06 | 180 | 67 | England | Accrington Stanley | 49 | 55 | 40000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 23.0 | 2015-04-08 | 2016.0 | 65.0 | 36.0 | 48.0 | 51.0 | 38.0 | 51.0 | 0.0 | NaN | 41 | 28 | 42 | 60 | 39 | 51 | 34 | 33 | 49 | 49 | 68 | 62 | 59 | 48 | 70 | 50 | 63 | 58 | 44 | 41 | 53 | 38 | 51 | 45 | 38 | 34 | 39 | 50 | 11 | 7 | 12 | 13 | 15 | 46 | 46 | 46 | 49 | 48 | 48 | 48 | 49 | 50 | 50 | 50 | 50 | 49 | 49 | 49 | 50 | 48 | 46 | 46 | 46 | 48 | 47 | 44 | 44 | 44 | 47 |
| 211732 | T. Thiele | 23 | 1991-07-31 | 188 | 75 | Germany | Burton Albion | 49 | 53 | 50000 | ST | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | SUB | 20.0 | 2015-07-01 | 2016.0 | 58.0 | 49.0 | 35.0 | 48.0 | 21.0 | 51.0 | 0.0 | NaN | 31 | 50 | 58 | 36 | 40 | 48 | 34 | 32 | 31 | 42 | 61 | 55 | 58 | 47 | 59 | 48 | 64 | 57 | 55 | 49 | 30 | 20 | 48 | 42 | 62 | 17 | 16 | 17 | 15 | 8 | 15 | 13 | 11 | 49 | 49 | 49 | 46 | 47 | 47 | 47 | 46 | 45 | 45 | 45 | 45 | 40 | 40 | 40 | 45 | 35 | 32 | 32 | 32 | 35 | 34 | 32 | 32 | 32 | 34 |
| 221498 | H. Al Mansour | 22 | 1993-05-19 | 180 | 70 | Saudi Arabia | Najran SC | 49 | 53 | 40000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | SUB | 20.0 | 2014-01-03 | 2021.0 | 66.0 | 39.0 | 49.0 | 47.0 | 40.0 | 62.0 | 0.0 | NaN | 38 | 35 | 51 | 60 | 38 | 48 | 34 | 37 | 52 | 43 | 65 | 67 | 45 | 56 | 66 | 54 | 61 | 57 | 66 | 33 | 59 | 38 | 51 | 51 | 46 | 40 | 40 | 45 | 8 | 11 | 8 | 11 | 13 | 49 | 49 | 49 | 48 | 49 | 49 | 49 | 48 | 49 | 49 | 49 | 50 | 49 | 49 | 49 | 50 | 47 | 48 | 48 | 48 | 47 | 47 | 48 | 48 | 48 | 47 |
| 229994 | J. Castillo | 29 | 1986-02-20 | 181 | 80 | Colombia | Cortuluá | 49 | 49 | 30000 | CDM | Right | 1 | 3 | 2 | Medium/Medium | Stocky | No | SUB | 17.0 | 2015-07-21 | 2021.0 | 56.0 | 34.0 | 45.0 | 47.0 | 47.0 | 61.0 | 0.0 | NaN | 42 | 27 | 45 | 53 | 32 | 42 | 30 | 31 | 53 | 50 | 57 | 56 | 55 | 48 | 60 | 45 | 56 | 51 | 69 | 34 | 56 | 40 | 31 | 35 | 48 | 46 | 50 | 53 | 11 | 14 | 5 | 7 | 13 | 43 | 43 | 43 | 44 | 42 | 42 | 42 | 44 | 44 | 44 | 44 | 45 | 46 | 46 | 46 | 45 | 48 | 49 | 49 | 49 | 48 | 49 | 51 | 51 | 51 | 49 |
| 228402 | T. Campbell | 18 | 1997-01-10 | 171 | 68 | England | West Bromwich Albion | 48 | 67 | 70000 | ST | Right | 1 | 3 | 3 | Medium/Medium | Lean | No | RES | 41.0 | 2015-07-01 | 2021.0 | 57.0 | 50.0 | 41.0 | 54.0 | 18.0 | 45.0 | 0.0 | NaN | 34 | 50 | 42 | 44 | 49 | 58 | 49 | 26 | 34 | 44 | 60 | 55 | 55 | 46 | 81 | 50 | 59 | 58 | 43 | 51 | 31 | 12 | 45 | 49 | 61 | 20 | 15 | 12 | 10 | 12 | 14 | 14 | 11 | 48 | 48 | 48 | 49 | 49 | 49 | 49 | 49 | 48 | 48 | 48 | 48 | 43 | 43 | 43 | 48 | 35 | 32 | 32 | 32 | 35 | 32 | 28 | 28 | 28 | 32 |
| 229589 | P. Denton | 18 | 1996-12-22 | 191 | 77 | England | Hartlepool United | 48 | 66 | 60000 | GK | Right | 1 | 2 | 1 | Medium/Medium | Lean | No | SUB | 31.0 | 2015-07-13 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.0 | NaN | 16 | 14 | 16 | 23 | 14 | 10 | 15 | 11 | 19 | 15 | 49 | 41 | 31 | 48 | 49 | 23 | 59 | 43 | 69 | 11 | 21 | 22 | 12 | 14 | 22 | 19 | 14 | 10 | 52 | 43 | 47 | 43 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 230734 | G. Doré | 18 | 1997-06-03 | 180 | 75 | France | Valenciennes FC | 48 | 66 | 60000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 34.0 | 2015-08-17 | 2018.0 | 57.0 | 34.0 | 48.0 | 50.0 | 45.0 | 53.0 | 0.0 | NaN | 39 | 28 | 44 | 55 | 30 | 47 | 38 | 35 | 53 | 51 | 60 | 55 | 56 | 53 | 65 | 50 | 60 | 46 | 55 | 32 | 56 | 49 | 32 | 48 | 44 | 41 | 42 | 54 | 11 | 8 | 9 | 15 | 5 | 43 | 43 | 43 | 46 | 45 | 45 | 45 | 46 | 47 | 47 | 47 | 47 | 48 | 48 | 48 | 47 | 49 | 50 | 50 | 50 | 49 | 49 | 49 | 49 | 49 | 49 |
| 222479 | J. Bonilla | 19 | 1996-04-16 | 183 | 77 | Colombia | Uniautónoma FC | 48 | 63 | 60000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Normal | No | SUB | 12.0 | 2014-02-01 | 2021.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 46.0 | NaN | 7 | 5 | 1 | 34 | -1 | 9 | 7 | 9 | 25 | 19 | 48 | 44 | 42 | 48 | 45 | 17 | 56 | 30 | 48 | 15 | 23 | 24 | 5 | 17 | 23 | 11 | 3 | 9 | 52 | 48 | 43 | 46 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 227829 | B. Murphy | 19 | 1995-12-22 | 176 | 75 | England | Bristol City | 48 | 63 | 70000 | CM | Left | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 26.0 | 2014-07-08 | 2016.0 | 65.0 | 37.0 | 48.0 | 47.0 | 41.0 | 56.0 | 0.0 | NaN | 35 | 28 | 47 | 57 | 31 | 45 | 36 | 36 | 51 | 44 | 64 | 65 | 57 | 47 | 74 | 54 | 58 | 56 | 58 | 40 | 51 | 44 | 46 | 47 | 44 | 37 | 40 | 46 | 7 | 14 | 9 | 16 | 15 | 46 | 46 | 46 | 46 | 47 | 47 | 47 | 46 | 48 | 48 | 48 | 48 | 48 | 48 | 48 | 48 | 47 | 47 | 47 | 47 | 47 | 47 | 46 | 46 | 46 | 47 |
| 228584 | A. May | 17 | 1997-12-06 | 183 | 73 | England | Portsmouth | 48 | 62 | 60000 | CM, CAM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 30.0 | 2014-07-04 | 2016.0 | 71.0 | 43.0 | 45.0 | 50.0 | 46.0 | 53.0 | 0.0 | NaN | 36 | 44 | 37 | 55 | 38 | 46 | 35 | 33 | 52 | 50 | 70 | 72 | 64 | 52 | 74 | 47 | 68 | 55 | 52 | 41 | 49 | 43 | 52 | 41 | 37 | 50 | 50 | 46 | 8 | 8 | 15 | 14 | 15 | 48 | 48 | 48 | 50 | 49 | 49 | 49 | 50 | 50 | 50 | 50 | 50 | 48 | 48 | 48 | 50 | 49 | 49 | 49 | 49 | 49 | 49 | 48 | 48 | 48 | 49 |
| 230722 | S. Boyd | 17 | 1998-06-20 | 191 | 75 | Republic of Ireland | Shamrock Rovers | 48 | 62 | 70000 | ST | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 18.0 | 2015-02-01 | 2016.0 | 69.0 | 46.0 | 39.0 | 46.0 | 22.0 | 57.0 | 0.0 | NaN | 46 | 41 | 38 | 40 | 42 | 44 | 49 | 50 | 42 | 41 | 81 | 88 | 49 | 36 | 67 | 48 | 48 | 72 | 55 | 45 | 39 | 27 | 40 | 39 | 33 | 9 | 18 | 6 | 8 | 10 | 5 | 10 | 6 | 48 | 48 | 48 | 47 | 47 | 47 | 47 | 47 | 45 | 45 | 45 | 47 | 41 | 41 | 41 | 47 | 38 | 35 | 35 | 35 | 38 | 36 | 33 | 33 | 33 | 36 |
| 213687 | L. Grimshaw | 20 | 1995-02-02 | 178 | 75 | England | Motherwell | 48 | 61 | 60000 | CDM, CB | Right | 1 | 2 | 2 | Medium/Medium | Normal | No | LCM | 4.0 | 2015-07-01 | 2016.0 | 58.0 | 36.0 | 43.0 | 45.0 | 44.0 | 59.0 | 0.0 | NaN | 33 | 32 | 42 | 50 | 27 | 41 | 33 | 37 | 42 | 44 | 63 | 54 | 58 | 54 | 65 | 49 | 63 | 55 | 59 | 33 | 65 | 41 | 36 | 43 | 44 | 41 | 47 | 49 | 13 | 8 | 15 | 12 | 11 | 43 | 43 | 43 | 44 | 44 | 44 | 44 | 44 | 44 | 44 | 44 | 45 | 45 | 45 | 45 | 45 | 47 | 48 | 48 | 48 | 47 | 47 | 49 | 49 | 49 | 47 |
| 225427 | S. Arango | 19 | 1996-01-27 | 180 | 70 | Colombia | Boyacá Chicó FC | 48 | 61 | 50000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Normal | No | SUB | 28.0 | 2014-09-01 | 2021.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 47.0 | NaN | 3 | 11 | 1 | 24 | 7 | 9 | 3 | -3 | 23 | 22 | 48 | 46 | 38 | 54 | 60 | 23 | 58 | 45 | 48 | 9 | 13 | 11 | -1 | 3 | 23 | 9 | 11 | -1 | 56 | 45 | 50 | 44 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 229869 | L. Roa | 19 | 1996-06-18 | 170 | 65 | Chile | CD Universidad de Concepción | 48 | 61 | 60000 | CM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 30.0 | 2015-06-02 | 2021.0 | 61.0 | 38.0 | 55.0 | 53.0 | 45.0 | 43.0 | 0.0 | NaN | 61 | 31 | 38 | 61 | 32 | 48 | 38 | 51 | 49 | 52 | 63 | 59 | 68 | 49 | 85 | 51 | 59 | 33 | 38 | 45 | 62 | 45 | 29 | 46 | 38 | 39 | 51 | 50 | 11 | 10 | 8 | 13 | 14 | 43 | 43 | 43 | 50 | 46 | 46 | 46 | 50 | 49 | 49 | 49 | 50 | 48 | 48 | 48 | 50 | 50 | 49 | 49 | 49 | 50 | 50 | 47 | 47 | 47 | 50 |
| 228222 | A. Ovalle | 18 | 1997-04-28 | 175 | 70 | Chile | Real Salt Lake | 48 | 60 | 50000 | CM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 26.0 | 2015-03-06 | 2021.0 | 62.0 | 36.0 | 46.0 | 49.0 | 40.0 | 53.0 | 0.0 | NaN | 39 | 28 | 42 | 56 | 41 | 45 | 35 | 35 | 48 | 50 | 55 | 67 | 56 | 56 | 75 | 52 | 63 | 62 | 47 | 36 | 53 | 43 | 48 | 46 | 41 | 40 | 38 | 47 | 8 | 10 | 13 | 13 | 16 | 45 | 45 | 45 | 47 | 47 | 47 | 47 | 47 | 48 | 48 | 48 | 49 | 48 | 48 | 48 | 49 | 48 | 47 | 47 | 47 | 48 | 48 | 45 | 45 | 45 | 48 |
| 221809 | G. Boylan | 19 | 1996-04-24 | 179 | 72 | Republic of Ireland | Sligo Rovers | 48 | 59 | 50000 | CB | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | SUB | 14.0 | 2013-12-13 | 2015.0 | 59.0 | 26.0 | 27.0 | 33.0 | 48.0 | 59.0 | 0.0 | NaN | 23 | 15 | 44 | 28 | 22 | 31 | 27 | 28 | 24 | 32 | 63 | 53 | 32 | 42 | 72 | 38 | 88 | 80 | 56 | 25 | 51 | 51 | 31 | 36 | 36 | 48 | 54 | 56 | 16 | 15 | 11 | 11 | 15 | 35 | 35 | 35 | 33 | 33 | 33 | 33 | 33 | 32 | 32 | 32 | 35 | 34 | 34 | 34 | 35 | 44 | 42 | 42 | 42 | 44 | 46 | 48 | 48 | 48 | 46 |
| 222072 | H. Hickford | 19 | 1996-06-23 | 183 | 70 | England | Milton Keynes Dons | 48 | 59 | 50000 | CB, RB | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 25.0 | 2014-02-18 | 2017.0 | 60.0 | 23.0 | 31.0 | 31.0 | 49.0 | 52.0 | 0.0 | NaN | 26 | 17 | 44 | 35 | 28 | 26 | 25 | 27 | 29 | 27 | 59 | 60 | 40 | 49 | 64 | 38 | 70 | 57 | 51 | 18 | 43 | 52 | 27 | 31 | 32 | 44 | 53 | 52 | 13 | 14 | 9 | 6 | 7 | 34 | 34 | 34 | 33 | 33 | 33 | 33 | 33 | 32 | 32 | 32 | 35 | 35 | 35 | 35 | 35 | 45 | 43 | 43 | 43 | 45 | 47 | 48 | 48 | 48 | 47 |
| 223091 | R. Donelon | 19 | 1996-04-17 | 171 | 71 | England | Sligo Rovers | 48 | 59 | 50000 | CB | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | LB | 19.0 | 2013-08-01 | 2015.0 | 53.0 | 24.0 | 34.0 | 32.0 | 49.0 | 55.0 | 0.0 | NaN | 26 | 9 | 60 | 41 | 13 | 30 | 29 | 25 | 37 | 30 | 52 | 54 | 35 | 43 | 59 | 40 | 54 | 57 | 57 | 22 | 50 | 46 | 29 | 35 | 36 | 52 | 53 | 50 | 15 | 15 | 13 | 10 | 15 | 35 | 35 | 35 | 33 | 33 | 33 | 33 | 33 | 33 | 33 | 33 | 35 | 36 | 36 | 36 | 35 | 43 | 43 | 43 | 43 | 43 | 45 | 48 | 48 | 48 | 45 |
| 223974 | J. Bayly | 19 | 1996-06-18 | 165 | 65 | Republic of Ireland | St. Patrick's Athletic | 48 | 59 | 50000 | CM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | SUB | 26.0 | 2013-08-01 | 2015.0 | 72.0 | 39.0 | 46.0 | 53.0 | 43.0 | 44.0 | 0.0 | NaN | 39 | 37 | 47 | 52 | 40 | 48 | 42 | 37 | 48 | 50 | 77 | 77 | 85 | 47 | 83 | 46 | 55 | 59 | 32 | 40 | 54 | 38 | 50 | 51 | 41 | 44 | 46 | 42 | 10 | 9 | 8 | 10 | 9 | 46 | 46 | 46 | 50 | 49 | 49 | 49 | 50 | 50 | 50 | 50 | 50 | 48 | 48 | 48 | 50 | 48 | 46 | 46 | 46 | 48 | 48 | 44 | 44 | 44 | 48 |
| 225639 | L. Ranelli | 19 | 1996-06-05 | 178 | 69 | Italy | Frosinone | 48 | 59 | 50000 | CM | Left | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 44.0 | 2014-12-29 | 2016.0 | 58.0 | 36.0 | 50.0 | 48.0 | 40.0 | 49.0 | 0.0 | NaN | 52 | 28 | 58 | 59 | 40 | 46 | 36 | 36 | 51 | 46 | 62 | 47 | 55 | 55 | 84 | 56 | 63 | 63 | 39 | 35 | 55 | 38 | 51 | 43 | 42 | 34 | 40 | 50 | 15 | 15 | 9 | 14 | 11 | 46 | 46 | 46 | 47 | 47 | 47 | 47 | 47 | 47 | 47 | 47 | 50 | 48 | 48 | 48 | 50 | 49 | 46 | 46 | 46 | 49 | 49 | 45 | 45 | 45 | 49 |
| 226018 | K. Tshimanga | 17 | 1997-07-22 | 180 | 78 | England | Milton Keynes Dons | 48 | 59 | 50000 | ST | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 27.0 | 2014-11-08 | 2016.0 | 61.0 | 48.0 | 35.0 | 48.0 | 19.0 | 52.0 | 0.0 | NaN | 27 | 51 | 46 | 39 | 40 | 49 | 41 | 32 | 27 | 42 | 64 | 58 | 57 | 48 | 65 | 41 | 55 | 56 | 56 | 52 | 37 | 15 | 51 | 45 | 46 | 3 | 5 | 7 | 8 | 14 | 17 | 10 | 12 | 48 | 48 | 48 | 47 | 48 | 48 | 48 | 47 | 46 | 46 | 46 | 45 | 40 | 40 | 40 | 45 | 34 | 31 | 31 | 31 | 34 | 33 | 30 | 30 | 30 | 33 |
| 229420 | C. Hall | 18 | 1997-03-23 | 180 | 75 | England | Plymouth Argyle | 48 | 59 | 50000 | LM | Left | 1 | 2 | 3 | High/High | Lean | No | RES | 22.0 | 2015-07-01 | 2016.0 | 63.0 | 38.0 | 46.0 | 47.0 | 40.0 | 56.0 | 0.0 | NaN | 49 | 32 | 41 | 51 | 37 | 49 | 42 | 38 | 42 | 37 | 62 | 64 | 55 | 45 | 65 | 50 | 61 | 65 | 55 | 37 | 46 | 38 | 51 | 42 | 42 | 35 | 44 | 44 | 11 | 5 | 13 | 9 | 14 | 45 | 45 | 45 | 47 | 46 | 46 | 46 | 47 | 46 | 46 | 46 | 48 | 45 | 45 | 45 | 48 | 48 | 44 | 44 | 44 | 48 | 47 | 44 | 44 | 44 | 47 |
| 219509 | A. Smith | 22 | 1993-05-25 | 185 | 78 | Republic of Ireland | Cork City | 48 | 58 | 50000 | GK | Right | 1 | 1 | 1 | Medium/Medium | Normal | No | SUB | 16.0 | 2015-02-17 | 2015.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.0 | NaN | 13 | 15 | 3 | 29 | 5 | 9 | 11 | -3 | 23 | 13 | 49 | 41 | 34 | 49 | 56 | 22 | 56 | 21 | 62 | -3 | 9 | 7 | -3 | 1 | 15 | 15 | 11 | 11 | 56 | 45 | 44 | 42 | 55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 222317 | F. Reilly | 18 | 1996-07-06 | 176 | 68 | Republic of Ireland | Drogheda United | 48 | 58 | 40000 | LM | Left | 1 | 3 | 2 | Medium/Medium | Normal | No | SUB | 26.0 | 2013-01-01 | 2015.0 | 64.0 | 44.0 | 44.0 | 49.0 | 27.0 | 38.0 | 0.0 | NaN | 45 | 45 | 37 | 48 | 34 | 49 | 39 | 33 | 43 | 46 | 63 | 64 | 53 | 37 | 71 | 51 | 31 | 55 | 33 | 36 | 32 | 30 | 44 | 39 | 50 | 22 | 28 | 24 | 10 | 11 | 12 | 15 | 11 | 45 | 45 | 45 | 47 | 46 | 46 | 46 | 47 | 46 | 46 | 46 | 48 | 43 | 43 | 43 | 48 | 41 | 37 | 37 | 37 | 41 | 39 | 32 | 32 | 32 | 39 |
| 223905 | P. Ng | 19 | 1996-04-27 | 180 | 77 | England | Crewe Alexandra | 48 | 58 | 40000 | CB | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 23.0 | 2014-07-01 | 2021.0 | 60.0 | 22.0 | 27.0 | 29.0 | 48.0 | 58.0 | 0.0 | NaN | 21 | 16 | 48 | 31 | 29 | 24 | 24 | 30 | 28 | 27 | 60 | 60 | 36 | 45 | 57 | 36 | 67 | 57 | 62 | 18 | 46 | 48 | 24 | 27 | 34 | 43 | 51 | 52 | 10 | 6 | 8 | 16 | 7 | 34 | 34 | 34 | 30 | 31 | 31 | 31 | 30 | 30 | 30 | 30 | 33 | 32 | 32 | 32 | 33 | 43 | 42 | 42 | 42 | 43 | 45 | 48 | 48 | 48 | 45 |
| 219676 | R. Lane | 18 | 1996-10-30 | 178 | 72 | England | Plymouth Argyle | 48 | 57 | 40000 | CM, LM | Left | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 25.0 | 2013-08-01 | 2021.0 | 76.0 | 52.0 | 50.0 | 45.0 | 47.0 | 53.0 | 0.0 | NaN | 40 | 48 | 41 | 57 | 54 | 41 | 59 | 64 | 50 | 40 | 70 | 80 | 71 | 49 | 72 | 53 | 74 | 53 | 58 | 63 | 35 | 48 | 45 | 46 | 57 | 48 | 50 | 46 | 17 | 10 | 16 | 12 | 10 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 48 | 48 | 48 | 49 | 50 | 48 | 48 | 48 | 50 | 50 | 48 | 48 | 48 | 50 |
| 225922 | M. Shelton | 18 | 1996-09-12 | 183 | 70 | England | Burton Albion | 48 | 57 | 40000 | RB | Right | 1 | 3 | 2 | Medium/Medium | Stocky | No | RES | 26.0 | 2014-10-23 | 2016.0 | 64.0 | 27.0 | 32.0 | 47.0 | 47.0 | 56.0 | 0.0 | NaN | 41 | 22 | 44 | 26 | 26 | 48 | 32 | 27 | 30 | 39 | 67 | 62 | 59 | 48 | 64 | 30 | 59 | 60 | 57 | 30 | 48 | 47 | 42 | 34 | 39 | 41 | 53 | 50 | 10 | 6 | 11 | 10 | 13 | 39 | 39 | 39 | 42 | 40 | 40 | 40 | 42 | 39 | 39 | 39 | 43 | 38 | 38 | 38 | 43 | 47 | 43 | 43 | 43 | 47 | 48 | 48 | 48 | 48 | 48 |
| 228581 | B. Kelly | 17 | 1997-07-26 | 183 | 82 | Republic of Ireland | Dundalk | 48 | 57 | 40000 | GK | Right | 1 | 1 | 1 | Medium/Medium | Normal | No | SUB | 30.0 | 2014-10-08 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.0 | NaN | 11 | 5 | 7 | 35 | 5 | 9 | 11 | 9 | 29 | 7 | 42 | 47 | 36 | 47 | 56 | 15 | 58 | 15 | 67 | -1 | 36 | 22 | 13 | 31 | 4 | -1 | -3 | 13 | 53 | 47 | 48 | 46 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 228293 | K. Bryce | 22 | 1993-04-16 | 183 | 84 | United States | Chicago Fire | 48 | 56 | 40000 | CDM | Right | 1 | 2 | 2 | Medium/High | Lean | No | RES | 27.0 | 2015-03-24 | 2021.0 | 55.0 | 34.0 | 45.0 | 48.0 | 45.0 | 57.0 | 0.0 | NaN | 37 | 32 | 51 | 55 | 33 | 47 | 34 | 37 | 52 | 51 | 55 | 55 | 43 | 51 | 55 | 43 | 62 | 34 | 71 | 35 | 52 | 49 | 32 | 38 | 39 | 44 | 45 | 48 | 12 | 14 | 11 | 7 | 14 | 44 | 44 | 44 | 44 | 44 | 44 | 44 | 44 | 45 | 45 | 45 | 45 | 45 | 45 | 45 | 45 | 46 | 48 | 48 | 48 | 46 | 46 | 50 | 50 | 50 | 46 |
| 228610 | O. Al Mazial | 22 | 1992-07-25 | 170 | 65 | Saudi Arabia | Al Shabab | 48 | 55 | 50000 | CM, RB | Right | 1 | 2 | 3 | Medium/Medium | Lean | No | RES | 4.0 | 2012-02-06 | 2017.0 | 74.0 | 33.0 | 50.0 | 53.0 | 44.0 | 48.0 | 0.0 | NaN | 51 | 27 | 46 | 58 | 30 | 50 | 32 | 41 | 49 | 51 | 73 | 75 | 68 | 49 | 70 | 46 | 58 | 60 | 39 | 34 | 55 | 44 | 33 | 47 | 44 | 41 | 49 | 50 | 11 | 13 | 12 | 9 | 7 | 43 | 43 | 43 | 49 | 46 | 46 | 46 | 49 | 48 | 48 | 48 | 51 | 48 | 48 | 48 | 51 | 52 | 49 | 49 | 49 | 52 | 52 | 47 | 47 | 47 | 52 |
| 228754 | J. Rivers | 21 | 1993-09-10 | 168 | 76 | England | Blackpool | 48 | 55 | 50000 | RM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | SUB | 7.0 | 2015-06-01 | 2016.0 | 60.0 | 43.0 | 45.0 | 51.0 | 29.0 | 49.0 | 0.0 | NaN | 45 | 39 | 42 | 42 | 49 | 49 | 44 | 43 | 40 | 47 | 61 | 60 | 58 | 43 | 80 | 51 | 42 | 50 | 55 | 42 | 35 | 18 | 47 | 57 | 48 | 28 | 29 | 37 | 14 | 11 | 14 | 11 | 8 | 47 | 47 | 47 | 48 | 48 | 48 | 48 | 48 | 48 | 48 | 48 | 48 | 44 | 44 | 44 | 48 | 40 | 37 | 37 | 37 | 40 | 39 | 36 | 36 | 36 | 39 |
| 230014 | E. Iandolo | 17 | 1997-08-05 | 178 | 71 | England | Swindon Town | 48 | 55 | 40000 | ST | Right | 1 | 2 | 3 | Medium/Medium | Lean | No | RES | 28.0 | 2015-08-04 | 2016.0 | 62.0 | 49.0 | 34.0 | 46.0 | 17.0 | 48.0 | 0.0 | NaN | 26 | 52 | 49 | 38 | 34 | 41 | 37 | 32 | 25 | 45 | 64 | 60 | 59 | 49 | 66 | 49 | 64 | 51 | 54 | 47 | 26 | 18 | 44 | 41 | 57 | 13 | 11 | 14 | 9 | 6 | 10 | 14 | 9 | 48 | 48 | 48 | 45 | 47 | 47 | 47 | 45 | 44 | 44 | 44 | 44 | 38 | 38 | 38 | 44 | 33 | 30 | 30 | 30 | 33 | 32 | 29 | 29 | 29 | 32 |
| 200633 | R. McDonald | 23 | 1992-04-11 | 191 | 82 | England | Northampton Town | 48 | 54 | 40000 | CB | Left | 1 | 2 | 2 | Medium/Medium | Normal | No | SUB | 18.0 | 2015-07-28 | 2016.0 | 61.0 | 26.0 | 30.0 | 28.0 | 44.0 | 65.0 | 0.0 | NaN | 25 | 20 | 54 | 33 | 23 | 22 | 13 | 25 | 31 | 25 | 59 | 62 | 46 | 41 | 57 | 35 | 72 | 62 | 69 | 29 | 59 | 40 | 23 | 35 | 29 | 42 | 46 | 44 | 11 | 13 | 6 | 16 | 7 | 35 | 35 | 35 | 32 | 32 | 32 | 32 | 32 | 31 | 31 | 31 | 34 | 33 | 33 | 33 | 34 | 41 | 41 | 41 | 41 | 41 | 43 | 48 | 48 | 48 | 43 |
| 225087 | F. Al Sagour | 19 | 1996-04-23 | 175 | 64 | Saudi Arabia | Najran SC | 48 | 54 | 40000 | LM | Left | 1 | 3 | 3 | Medium/Medium | Lean | No | RES | 24.0 | 2014-01-01 | 2021.0 | 62.0 | 40.0 | 43.0 | 54.0 | 27.0 | 39.0 | 0.0 | NaN | 49 | 38 | 32 | 39 | 45 | 56 | 45 | 37 | 41 | 50 | 62 | 62 | 57 | 45 | 67 | 53 | 42 | 46 | 37 | 32 | 36 | 7 | 41 | 49 | 49 | 30 | 30 | 34 | 8 | 8 | 13 | 12 | 15 | 44 | 44 | 44 | 48 | 46 | 46 | 46 | 48 | 46 | 46 | 46 | 48 | 42 | 42 | 42 | 48 | 40 | 35 | 35 | 35 | 40 | 38 | 32 | 32 | 32 | 38 |
| 225342 | J. Stevens | 17 | 1997-08-02 | 188 | 77 | England | Oxford United | 48 | 54 | 30000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Lean | No | RES | 35.0 | 2014-07-01 | 2021.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 43.0 | NaN | 9 | 15 | 3 | 26 | -1 | -3 | 11 | -3 | 26 | 15 | 40 | 45 | 36 | 48 | 53 | 25 | 53 | 7 | 61 | 3 | 24 | 13 | 13 | 25 | 24 | 7 | -1 | 7 | 56 | 49 | 44 | 40 | 54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 230779 | A. Fox | 22 | 1993-01-15 | 183 | 64 | England | Peterborough United | 48 | 54 | 50000 | LM, LB, LWB | Left | 1 | 2 | 2 | High/Medium | Normal | No | RES | 18.0 | 2015-08-01 | 2017.0 | 57.0 | 39.0 | 50.0 | 46.0 | 38.0 | 49.0 | 0.0 | NaN | 53 | 31 | 45 | 55 | 37 | 47 | 41 | 35 | 54 | 38 | 55 | 58 | 51 | 49 | 67 | 53 | 59 | 55 | 43 | 38 | 52 | 40 | 52 | 40 | 43 | 36 | 35 | 44 | 5 | 12 | 7 | 14 | 10 | 45 | 45 | 45 | 47 | 46 | 46 | 46 | 47 | 46 | 46 | 46 | 48 | 46 | 46 | 46 | 48 | 47 | 45 | 45 | 45 | 47 | 46 | 43 | 43 | 43 | 46 |
| 230804 | M. Al Sudani | 25 | 1989-10-28 | 170 | 70 | Saudi Arabia | Al Faisaly | 48 | 54 | 40000 | CM | Right | 1 | 3 | 3 | Medium/Medium | Normal | No | SUB | 22.0 | 2015-06-17 | 2018.0 | 61.0 | 36.0 | 50.0 | 45.0 | 36.0 | 58.0 | 0.0 | NaN | 41 | 27 | 45 | 60 | 34 | 41 | 35 | 35 | 54 | 43 | 51 | 69 | 53 | 47 | 81 | 51 | 55 | 62 | 57 | 36 | 57 | 36 | 49 | 47 | 42 | 30 | 35 | 49 | 6 | 11 | 14 | 8 | 13 | 45 | 45 | 45 | 46 | 46 | 46 | 46 | 46 | 47 | 47 | 47 | 48 | 48 | 48 | 48 | 48 | 47 | 47 | 47 | 47 | 47 | 46 | 44 | 44 | 44 | 46 |
| 230126 | J. Brophy | 20 | 1994-07-25 | 180 | 68 | England | Swindon Town | 48 | 53 | 40000 | LB, LM | Left | 1 | 2 | 2 | Medium/Medium | Normal | No | SUB | 15.0 | 2015-08-06 | 2015.0 | 64.0 | 26.0 | 37.0 | 50.0 | 46.0 | 54.0 | 0.0 | NaN | 46 | 28 | 42 | 34 | 19 | 51 | 36 | 33 | 33 | 41 | 68 | 61 | 60 | 46 | 69 | 26 | 63 | 54 | 54 | 20 | 53 | 44 | 37 | 36 | 31 | 43 | 51 | 51 | 8 | 10 | 9 | 7 | 6 | 39 | 39 | 39 | 44 | 41 | 41 | 41 | 44 | 40 | 40 | 40 | 44 | 39 | 39 | 39 | 44 | 48 | 44 | 44 | 44 | 48 | 48 | 48 | 48 | 48 | 48 |
| 228683 | F. Almoqati | 24 | 1990-08-10 | 170 | 69 | Saudi Arabia | Al Qadisiyah | 48 | 52 | 40000 | CDM | Right | 1 | 3 | 3 | Medium/Medium | Lean | No | SUB | 27.0 | 2013-01-01 | 2021.0 | 59.0 | 25.0 | 37.0 | 48.0 | 53.0 | 65.0 | 0.0 | NaN | 44 | 21 | 42 | 37 | 23 | 49 | 32 | 32 | 27 | 42 | 64 | 55 | 50 | 47 | 77 | 28 | 62 | 70 | 70 | 28 | 47 | 54 | 41 | 39 | 31 | 46 | 61 | 61 | 12 | 13 | 7 | 7 | 7 | 39 | 39 | 39 | 43 | 41 | 41 | 41 | 43 | 41 | 41 | 41 | 44 | 42 | 42 | 42 | 44 | 52 | 48 | 48 | 48 | 52 | 53 | 53 | 53 | 53 | 53 |
| 222012 | F. Al Sobaie | 27 | 1988-01-01 | 168 | 68 | Saudi Arabia | Najran SC | 48 | 50 | 30000 | RB, CAM, RM | Right | 1 | 2 | 2 | Medium/Medium | Normal | No | SUB | 15.0 | 2014-07-08 | 2021.0 | 56.0 | 28.0 | 29.0 | 45.0 | 54.0 | 56.0 | 0.0 | NaN | 34 | 27 | 44 | 21 | 28 | 46 | 26 | 28 | 30 | 34 | 55 | 56 | 60 | 46 | 79 | 25 | 65 | 53 | 58 | 30 | 52 | 49 | 38 | 40 | 35 | 53 | 59 | 61 | 9 | 11 | 12 | 9 | 7 | 37 | 37 | 37 | 39 | 38 | 38 | 38 | 39 | 37 | 37 | 37 | 39 | 37 | 37 | 37 | 39 | 46 | 44 | 44 | 44 | 46 | 48 | 52 | 52 | 52 | 48 |
| 228676 | J. Dyche | 17 | 1997-10-11 | 175 | 65 | England | Scunthorpe United | 47 | 68 | 70000 | CAM, LB | Left | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 24.0 | 2015-03-05 | 2016.0 | 63.0 | 44.0 | 41.0 | 52.0 | 44.0 | 56.0 | 0.0 | NaN | 41 | 48 | 45 | 44 | 22 | 55 | 31 | 29 | 42 | 42 | 61 | 64 | 59 | 46 | 78 | 38 | 62 | 59 | 56 | 47 | 51 | 47 | 49 | 39 | 43 | 37 | 46 | 50 | 13 | 9 | 15 | 11 | 9 | 48 | 48 | 48 | 49 | 48 | 48 | 48 | 49 | 47 | 47 | 47 | 49 | 45 | 45 | 45 | 49 | 48 | 46 | 46 | 46 | 48 | 48 | 47 | 47 | 47 | 48 |
| 227529 | M. Hudson | 16 | 1998-07-29 | 184 | 70 | England | Preston North End | 47 | 65 | 60000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Lean | No | RES | 31.0 | 2015-02-03 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.0 | NaN | 19 | 18 | 11 | 29 | 13 | 12 | 20 | 11 | 16 | 20 | 49 | 42 | 37 | 49 | 54 | 20 | 51 | 35 | 45 | 19 | 17 | 24 | 19 | 15 | 17 | 16 | 13 | 20 | 46 | 46 | 42 | 45 | 53 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 230609 | S. Fogarty | 17 | 1997-08-16 | 183 | 80 | Republic of Ireland | Bray Wanderers | 47 | 64 | 60000 | GK | Right | 1 | 1 | 1 | Medium/Medium | Lean | No | SUB | 50.0 | 2015-07-10 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 46.0 | NaN | 7 | -3 | -5 | 23 | -1 | 5 | 9 | 9 | 7 | 13 | 42 | 51 | 40 | 51 | 73 | 11 | 72 | 9 | 54 | 5 | 22 | 11 | -3 | 29 | 0 | 1 | -3 | 13 | 46 | 43 | 51 | 49 | 49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 227931 | J. Storer | 17 | 1998-01-02 | 185 | 70 | England | Stevenage | 47 | 61 | 60000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 38.0 | 2015-01-01 | 2016.0 | 67.0 | 36.0 | 45.0 | 47.0 | 42.0 | 58.0 | 0.0 | NaN | 33 | 29 | 48 | 56 | 35 | 48 | 35 | 32 | 49 | 43 | 65 | 68 | 53 | 44 | 61 | 51 | 55 | 62 | 57 | 31 | 58 | 45 | 45 | 42 | 42 | 37 | 43 | 46 | 14 | 7 | 12 | 11 | 9 | 45 | 45 | 45 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 48 | 47 | 47 | 47 | 48 | 48 | 48 | 48 | 48 | 48 | 48 | 47 | 47 | 47 | 48 |
| 229560 | C. Chambers | 17 | 1998-01-11 | 183 | 74 | Republic of Ireland | Bray Wanderers | 47 | 61 | 60000 | GK | Right | 1 | 1 | 1 | Medium/Medium | Lean | No | RES | 51.0 | 2015-07-02 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 41.0 | NaN | 11 | -3 | 13 | 24 | 9 | 5 | 7 | 13 | 21 | 23 | 41 | 41 | 39 | 47 | 61 | 13 | 56 | 24 | 55 | 5 | 13 | 20 | -5 | 30 | 4 | 5 | 1 | 13 | 50 | 45 | 48 | 46 | 49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 229669 | Matheus Silva | 18 | 1996-12-08 | 188 | 90 | Brazil | San Jose Earthquakes | 47 | 61 | 60000 | CDM, CB | Right | 1 | 3 | 2 | Medium/High | Lean | No | RES | 49.0 | 2015-07-15 | 2021.0 | 55.0 | 32.0 | 42.0 | 43.0 | 47.0 | 63.0 | 0.0 | NaN | 38 | 29 | 42 | 46 | 29 | 43 | 30 | 34 | 45 | 41 | 55 | 55 | 41 | 50 | 45 | 40 | 61 | 34 | 83 | 31 | 50 | 49 | 31 | 42 | 35 | 43 | 49 | 49 | 10 | 13 | 9 | 12 | 7 | 41 | 41 | 41 | 42 | 41 | 41 | 41 | 42 | 42 | 42 | 42 | 42 | 43 | 43 | 43 | 42 | 45 | 47 | 47 | 47 | 45 | 46 | 51 | 51 | 51 | 46 |
| 229850 | C. Toonga | 17 | 1997-11-20 | 175 | 76 | England | AFC Wimbledon | 47 | 61 | 60000 | CM, LM | Left | 1 | 2 | 2 | Medium/Medium | Normal | No | RES | 28.0 | 2015-06-12 | 2016.0 | 71.0 | 39.0 | 44.0 | 52.0 | 49.0 | 55.0 | 0.0 | NaN | 37 | 44 | 48 | 47 | 28 | 50 | 40 | 32 | 43 | 52 | 69 | 72 | 58 | 47 | 65 | 44 | 74 | 58 | 56 | 28 | 43 | 43 | 32 | 48 | 40 | 48 | 54 | 48 | 13 | 13 | 12 | 14 | 7 | 47 | 47 | 47 | 48 | 47 | 47 | 47 | 48 | 48 | 48 | 48 | 49 | 47 | 47 | 47 | 49 | 50 | 48 | 48 | 48 | 50 | 51 | 50 | 50 | 50 | 51 |
| 230611 | D. Burns | 18 | 1997-07-01 | 185 | 70 | England | Cambridge United | 47 | 60 | 50000 | CB | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 29.0 | 2015-07-01 | 2018.0 | 55.0 | 26.0 | 27.0 | 32.0 | 49.0 | 51.0 | 0.0 | NaN | 29 | 24 | 46 | 27 | 20 | 28 | 24 | 28 | 27 | 27 | 56 | 55 | 44 | 48 | 61 | 36 | 60 | 60 | 50 | 21 | 41 | 51 | 28 | 25 | 35 | 47 | 49 | 50 | 7 | 7 | 9 | 11 | 9 | 35 | 35 | 35 | 32 | 32 | 32 | 32 | 32 | 30 | 30 | 30 | 34 | 33 | 33 | 33 | 34 | 44 | 41 | 41 | 41 | 44 | 46 | 47 | 47 | 47 | 46 |
| 225118 | S. Austin | 18 | 1996-09-01 | 182 | 70 | England | Burton Albion | 47 | 58 | 50000 | ST | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 27.0 | 2014-08-15 | 2021.0 | 69.0 | 46.0 | 41.0 | 49.0 | 17.0 | 43.0 | 0.0 | NaN | 36 | 50 | 45 | 44 | 33 | 49 | 36 | 37 | 40 | 46 | 66 | 71 | 59 | 50 | 55 | 43 | 58 | 54 | 43 | 44 | 28 | 1 | 46 | 48 | 59 | 5 | 3 | -3 | 13 | 10 | 11 | 13 | 10 | 47 | 47 | 47 | 49 | 48 | 48 | 48 | 49 | 48 | 48 | 48 | 48 | 42 | 42 | 42 | 48 | 35 | 31 | 31 | 31 | 35 | 33 | 27 | 27 | 27 | 33 |
| 228104 | M. O'Connor | 16 | 1998-07-31 | 181 | 77 | Republic of Ireland | Dundalk | 47 | 58 | 50000 | ST | Right | 1 | 3 | 3 | Medium/Medium | Normal | No | RES | 26.0 | 2015-01-01 | 2016.0 | 55.0 | 48.0 | 35.0 | 46.0 | 22.0 | 43.0 | 0.0 | NaN | 30 | 55 | 49 | 39 | 40 | 41 | 37 | 27 | 30 | 48 | 52 | 52 | 59 | 48 | 63 | 42 | 74 | 53 | 40 | 45 | 31 | 15 | 48 | 45 | 43 | 13 | 11 | 9 | 10 | 8 | 8 | 15 | 16 | 47 | 47 | 47 | 45 | 46 | 46 | 46 | 45 | 45 | 45 | 45 | 44 | 40 | 40 | 40 | 44 | 34 | 32 | 32 | 32 | 34 | 33 | 30 | 30 | 30 | 33 |
| 229975 | M. Argasiński | 19 | 1995-08-01 | 187 | 74 | Poland | Cracovia | 47 | 58 | 50000 | CM | Right | 1 | 3 | 3 | Medium/Medium | Lean | No | SUB | 17.0 | 2013-03-05 | 2016.0 | 54.0 | 34.0 | 50.0 | 46.0 | 46.0 | 53.0 | 0.0 | NaN | 39 | 30 | 50 | 59 | 30 | 44 | 34 | 36 | 52 | 44 | 53 | 54 | 53 | 48 | 56 | 47 | 60 | 44 | 56 | 31 | 53 | 43 | 32 | 51 | 39 | 48 | 42 | 55 | 10 | 8 | 11 | 10 | 13 | 43 | 43 | 43 | 44 | 44 | 44 | 44 | 44 | 46 | 46 | 46 | 46 | 47 | 47 | 47 | 46 | 47 | 49 | 49 | 49 | 47 | 48 | 49 | 49 | 49 | 48 |
| 208365 | J. Dykes | 20 | 1995-06-30 | 178 | 68 | Republic of Ireland | Sligo Rovers | 47 | 57 | 50000 | LB, CB | Left | 1 | 2 | 2 | Low/Medium | Normal | No | SUB | 15.0 | 2012-01-01 | 2015.0 | 55.0 | 20.0 | 33.0 | 26.0 | 50.0 | 53.0 | 0.0 | NaN | 30 | 7 | 47 | 40 | 13 | 13 | 33 | 30 | 34 | 26 | 55 | 55 | 33 | 44 | 90 | 31 | 61 | 96 | 48 | 5 | 48 | 49 | 13 | 28 | 45 | 55 | 54 | 55 | 9 | 14 | 16 | 17 | 15 | 30 | 30 | 30 | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 33 | 34 | 34 | 34 | 33 | 45 | 44 | 44 | 44 | 45 | 47 | 48 | 48 | 48 | 47 |
| 228745 | C. Riascos | 21 | 1994-06-19 | 178 | 74 | Colombia | Alianza Petrolera | 47 | 57 | 50000 | RB | Right | 1 | 4 | 2 | Medium/Medium | Normal | No | RES | 4.0 | 2015-01-01 | 2021.0 | 59.0 | 26.0 | 28.0 | 31.0 | 51.0 | 60.0 | 0.0 | NaN | 17 | 25 | 43 | 34 | 30 | 24 | 30 | 28 | 26 | 26 | 55 | 63 | 84 | 46 | 64 | 40 | 68 | 59 | 74 | 13 | 50 | 56 | 15 | 31 | 33 | 54 | 55 | 62 | 17 | 13 | 11 | 13 | 11 | 34 | 34 | 34 | 31 | 31 | 31 | 31 | 31 | 31 | 31 | 31 | 32 | 33 | 33 | 33 | 32 | 45 | 44 | 44 | 44 | 45 | 47 | 51 | 51 | 51 | 47 |
| 222361 | W. Randall | 18 | 1997-05-02 | 180 | 65 | England | Swindon Town | 47 | 56 | 40000 | CM, LWB | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 21.0 | 2014-05-03 | 2017.0 | 64.0 | 39.0 | 49.0 | 49.0 | 38.0 | 40.0 | 0.0 | NaN | 41 | 37 | 43 | 54 | 36 | 44 | 45 | 44 | 51 | 46 | 65 | 63 | 73 | 48 | 60 | 41 | 55 | 48 | 37 | 40 | 36 | 28 | 44 | 51 | 46 | 34 | 45 | 47 | 15 | 9 | 8 | 8 | 15 | 45 | 45 | 45 | 48 | 47 | 47 | 47 | 48 | 49 | 49 | 49 | 49 | 47 | 47 | 47 | 49 | 45 | 44 | 44 | 44 | 45 | 45 | 41 | 41 | 41 | 45 |
| 225415 | G. Casey | 17 | 1997-07-08 | 182 | 70 | England | Stevenage | 47 | 56 | 30000 | RB | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 26.0 | 2014-01-01 | 2016.0 | 72.0 | 35.0 | 42.0 | 47.0 | 39.0 | 53.0 | 0.0 | NaN | 39 | 30 | 43 | 46 | 32 | 47 | 37 | 35 | 44 | 41 | 73 | 71 | 65 | 40 | 70 | 48 | 55 | 63 | 48 | 33 | 54 | 42 | 51 | 42 | 36 | 39 | 42 | 50 | 14 | 13 | 12 | 12 | 15 | 44 | 44 | 44 | 46 | 45 | 45 | 45 | 46 | 45 | 45 | 45 | 47 | 44 | 44 | 44 | 47 | 47 | 44 | 44 | 44 | 47 | 47 | 43 | 43 | 43 | 47 |
| 228794 | M. Alnajrani | 21 | 1993-07-23 | 175 | 70 | Saudi Arabia | Al Qadisiyah | 47 | 56 | 50000 | LW | Right | 1 | 3 | 3 | Medium/Medium | Lean | No | SUB | 20.0 | 2012-01-01 | 2021.0 | 52.0 | 55.0 | 36.0 | 49.0 | 21.0 | 60.0 | 0.0 | NaN | 27 | 62 | 57 | 37 | 38 | 46 | 40 | 28 | 32 | 49 | 52 | 52 | 52 | 52 | 75 | 53 | 66 | 57 | 72 | 47 | 34 | 19 | 52 | 45 | 60 | 18 | 13 | 18 | 11 | 8 | 7 | 7 | 10 | 54 | 54 | 54 | 47 | 50 | 50 | 50 | 47 | 47 | 47 | 47 | 46 | 42 | 42 | 42 | 46 | 34 | 33 | 33 | 33 | 34 | 33 | 34 | 34 | 34 | 33 |
| 222914 | A. Hawtin | 20 | 1995-06-13 | 178 | 76 | England | Oxford United | 47 | 55 | 50000 | RM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 32.0 | 2014-05-06 | 2021.0 | 64.0 | 45.0 | 41.0 | 52.0 | 23.0 | 40.0 | 0.0 | NaN | 42 | 26 | 41 | 43 | 46 | 51 | 46 | 33 | 39 | 55 | 67 | 62 | 58 | 37 | 67 | 64 | 42 | 42 | 43 | 41 | 32 | 24 | 45 | 42 | 49 | 11 | 24 | 22 | 10 | 12 | 9 | 11 | 10 | 47 | 47 | 47 | 48 | 48 | 48 | 48 | 48 | 46 | 46 | 46 | 47 | 41 | 41 | 41 | 47 | 37 | 34 | 34 | 34 | 37 | 36 | 31 | 31 | 31 | 36 |
| 225500 | M. Loaiza | 20 | 1995-04-06 | 182 | 79 | Colombia | Deportivo Cali | 47 | 55 | 40000 | GK | Left | 1 | 3 | 1 | Medium/Medium | Normal | No | SUB | 32.0 | 2014-07-03 | 2021.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.0 | NaN | 11 | 13 | 1 | 28 | -3 | 1 | 9 | 3 | 24 | 24 | 49 | 41 | 39 | 53 | 55 | 23 | 57 | 31 | 65 | 15 | 13 | 26 | 15 | 19 | 24 | 1 | 15 | 3 | 53 | 44 | 47 | 40 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 228719 | A. Almutair | 23 | 1992-06-12 | 165 | 66 | Saudi Arabia | Al Qadisiyah | 47 | 55 | 50000 | ST | Left | 1 | 3 | 2 | Medium/Low | Normal | No | SUB | 7.0 | 2013-01-01 | 2021.0 | 60.0 | 40.0 | 50.0 | 46.0 | 43.0 | 48.0 | 0.0 | NaN | 37 | 33 | 48 | 60 | 39 | 43 | 40 | 37 | 52 | 42 | 68 | 54 | 55 | 48 | 79 | 55 | 62 | 55 | 39 | 40 | 59 | 45 | 54 | 49 | 37 | 38 | 44 | 41 | 8 | 7 | 11 | 11 | 10 | 47 | 47 | 47 | 47 | 48 | 48 | 48 | 47 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 47 | 48 | 48 | 48 | 47 | 47 | 46 | 46 | 46 | 47 |
| 228692 | H. Jafari | 23 | 1991-12-06 | 167 | 65 | Saudi Arabia | Al Qadisiyah | 47 | 54 | 40000 | CDM | Left | 1 | 3 | 2 | Medium/Medium | Lean | No | LCM | 14.0 | 2014-01-01 | 2021.0 | 63.0 | 33.0 | 46.0 | 49.0 | 43.0 | 46.0 | 0.0 | NaN | 39 | 26 | 48 | 52 | 31 | 43 | 34 | 36 | 49 | 50 | 62 | 64 | 51 | 55 | 88 | 46 | 62 | 48 | 39 | 33 | 56 | 44 | 38 | 45 | 43 | 36 | 44 | 51 | 10 | 10 | 16 | 11 | 12 | 43 | 43 | 43 | 46 | 45 | 45 | 45 | 46 | 46 | 46 | 46 | 47 | 47 | 47 | 47 | 47 | 48 | 47 | 47 | 47 | 48 | 49 | 46 | 46 | 46 | 49 |
| 228722 | A. Albalawi | 24 | 1990-09-12 | 170 | 75 | Saudi Arabia | Al Qadisiyah | 47 | 52 | 40000 | CDM | Right | 1 | 3 | 3 | Medium/Medium | Normal | No | RES | 12.0 | 2014-01-01 | 2021.0 | 51.0 | 37.0 | 57.0 | 50.0 | 34.0 | 62.0 | 0.0 | NaN | 47 | 36 | 41 | 64 | 37 | 44 | 44 | 43 | 67 | 55 | 52 | 50 | 54 | 44 | 68 | 41 | 58 | 47 | 74 | 29 | 50 | 21 | 45 | 54 | 45 | 34 | 36 | 42 | 7 | 8 | 12 | 11 | 12 | 46 | 46 | 46 | 48 | 47 | 47 | 47 | 48 | 51 | 51 | 51 | 50 | 51 | 51 | 51 | 50 | 44 | 47 | 47 | 47 | 44 | 43 | 43 | 43 | 43 | 43 |
| 210187 | S. Amedo | 32 | 1982-07-12 | 176 | 75 | Saudi Arabia | Al Wehda | 47 | 47 | 20000 | CB | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 17.0 | 2004-01-01 | 2020.0 | 32.0 | 20.0 | 25.0 | 31.0 | 46.0 | 65.0 | 0.0 | NaN | 20 | 14 | 45 | 25 | 26 | 26 | 26 | 21 | 28 | 26 | 33 | 32 | 49 | 43 | 62 | 38 | 65 | 58 | 72 | 13 | 56 | 43 | 16 | 28 | 32 | 43 | 49 | 49 | 9 | 15 | 12 | 12 | 12 | 29 | 29 | 29 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 28 | 30 | 30 | 30 | 28 | 38 | 40 | 40 | 40 | 38 | 40 | 47 | 47 | 47 | 40 |
| 225319 | N. McLaughlin | 17 | 1998-01-01 | 177 | 70 | Scotland | Partick Thistle FC | 46 | 63 | 70000 | ST, CAM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 26.0 | 2015-05-26 | 2016.0 | 67.0 | 42.0 | 44.0 | 45.0 | 29.0 | 56.0 | 0.0 | NaN | 33 | 46 | 34 | 50 | 41 | 42 | 34 | 30 | 48 | 44 | 70 | 65 | 50 | 40 | 72 | 48 | 57 | 57 | 56 | 29 | 52 | 31 | 44 | 47 | 39 | 22 | 33 | 31 | 8 | 16 | 15 | 14 | 9 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 47 | 44 | 44 | 44 | 47 | 41 | 41 | 41 | 41 | 41 | 40 | 38 | 38 | 38 | 40 |
| 230721 | S. Hanney | 17 | 1998-02-19 | 182 | 74 | Republic of Ireland | Shamrock Rovers | 46 | 63 | 60000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 19.0 | 2015-02-01 | 2018.0 | 69.0 | 42.0 | 43.0 | 48.0 | 44.0 | 51.0 | 0.0 | NaN | 37 | 57 | 42 | 41 | 42 | 48 | 42 | 50 | 37 | 49 | 76 | 76 | 49 | 35 | 58 | 45 | 64 | 83 | 31 | 52 | 35 | 49 | 35 | 45 | 50 | 47 | 55 | 32 | 14 | 5 | 12 | 13 | 14 | 46 | 46 | 46 | 48 | 47 | 47 | 47 | 48 | 47 | 47 | 47 | 49 | 46 | 46 | 46 | 49 | 48 | 46 | 46 | 46 | 48 | 48 | 45 | 45 | 45 | 48 |
| 225005 | R. McManus | 19 | 1996-06-15 | 178 | 70 | Republic of Ireland | Sligo Rovers | 46 | 61 | 70000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | SUB | 26.0 | 2013-08-01 | 2015.0 | 69.0 | 43.0 | 44.0 | 48.0 | 41.0 | 48.0 | 0.0 | NaN | 40 | 41 | 45 | 49 | 40 | 46 | 33 | 33 | 45 | 47 | 72 | 66 | 56 | 43 | 62 | 49 | 54 | 60 | 44 | 44 | 41 | 39 | 46 | 44 | 38 | 41 | 44 | 36 | 15 | 11 | 6 | 8 | 14 | 47 | 47 | 47 | 48 | 48 | 48 | 48 | 48 | 48 | 48 | 48 | 49 | 46 | 46 | 46 | 49 | 47 | 44 | 44 | 44 | 47 | 46 | 43 | 43 | 43 | 46 |
| 225006 | G. Armstrong | 19 | 1996-01-28 | 180 | 71 | Republic of Ireland | Sligo Rovers | 46 | 60 | 70000 | CM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | SUB | 20.0 | 2013-08-01 | 2015.0 | 65.0 | 39.0 | 45.0 | 46.0 | 42.0 | 47.0 | 0.0 | NaN | 41 | 34 | 45 | 48 | 39 | 46 | 33 | 34 | 45 | 43 | 66 | 65 | 53 | 46 | 56 | 45 | 54 | 57 | 42 | 40 | 43 | 38 | 46 | 47 | 44 | 41 | 46 | 36 | 7 | 6 | 9 | 7 | 7 | 45 | 45 | 45 | 47 | 46 | 46 | 46 | 47 | 46 | 46 | 46 | 48 | 46 | 46 | 46 | 48 | 46 | 44 | 44 | 44 | 46 | 46 | 43 | 43 | 43 | 46 |
| 225331 | D. Henry | 17 | 1997-09-12 | 180 | 65 | England | Peterborough United | 46 | 58 | 40000 | GK | Right | 1 | 2 | 1 | Medium/Medium | Normal | No | SUB | 26.0 | 2014-08-01 | 2017.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 32.0 | NaN | 7 | 13 | -3 | 29 | 3 | 7 | 7 | 15 | 24 | 11 | 29 | 34 | 36 | 52 | 71 | 15 | 50 | 29 | 42 | 15 | 26 | 1 | -9 | 19 | 23 | 13 | 11 | 11 | 47 | 43 | 49 | 40 | 53 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 228155 | C. Johns | 18 | 1997-06-22 | 182 | 71 | England | Swindon Town | 46 | 57 | 40000 | GK | Right | 1 | 2 | 1 | Medium/Medium | Normal | No | RES | 35.0 | 2015-03-14 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 43.0 | NaN | 9 | -3 | 1 | 22 | 5 | -1 | -1 | 15 | 11 | 25 | 43 | 43 | 36 | 52 | 64 | 15 | 58 | -9 | 50 | -1 | 7 | 25 | 11 | 13 | 22 | 3 | 13 | 9 | 43 | 47 | 39 | 42 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 229414 | Ibargoien | 19 | 1995-07-13 | 180 | 75 | Spain | SD Eibar | 46 | 56 | 50000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 26.0 | 2015-07-01 | 2016.0 | 55.0 | 31.0 | 45.0 | 48.0 | 40.0 | 54.0 | 0.0 | NaN | 33 | 27 | 41 | 52 | 26 | 47 | 36 | 34 | 51 | 44 | 54 | 55 | 58 | 53 | 65 | 47 | 56 | 49 | 56 | 25 | 53 | 44 | 29 | 47 | 40 | 35 | 38 | 51 | 7 | 10 | 10 | 7 | 6 | 41 | 41 | 41 | 43 | 43 | 43 | 43 | 43 | 45 | 45 | 45 | 45 | 46 | 46 | 46 | 45 | 46 | 47 | 47 | 47 | 46 | 45 | 45 | 45 | 45 | 45 |
| 225158 | T. Holland | 18 | 1997-06-08 | 176 | 71 | England | Swindon Town | 46 | 55 | 40000 | RB | Right | 1 | 2 | 2 | Medium/Medium | Normal | No | RES | 33.0 | 2014-08-23 | 2016.0 | 60.0 | 26.0 | 31.0 | 46.0 | 45.0 | 53.0 | 0.0 | NaN | 37 | 24 | 39 | 29 | 20 | 45 | 27 | 32 | 27 | 39 | 68 | 54 | 59 | 42 | 73 | 29 | 57 | 54 | 55 | 23 | 45 | 47 | 41 | 33 | 39 | 35 | 52 | 52 | 15 | 8 | 11 | 14 | 10 | 38 | 38 | 38 | 40 | 38 | 38 | 38 | 40 | 38 | 38 | 38 | 41 | 37 | 37 | 37 | 41 | 45 | 41 | 41 | 41 | 45 | 46 | 46 | 46 | 46 | 46 |
| 229393 | S. Hornby | 20 | 1995-02-14 | 188 | 80 | England | Burton Albion | 46 | 55 | 50000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Normal | No | RES | 28.0 | 2015-05-05 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 46.0 | NaN | 12 | 13 | 13 | 27 | 13 | 14 | 13 | 14 | 19 | 15 | 46 | 46 | 38 | 51 | 51 | 22 | 58 | 24 | 66 | 17 | 18 | 22 | 12 | 21 | 26 | 17 | 11 | 15 | 49 | 43 | 45 | 41 | 48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 224927 | T. Smith | 17 | 1998-02-25 | 176 | 70 | England | Swindon Town | 46 | 54 | 40000 | CM | Right | 1 | 2 | 3 | Medium/Medium | Normal | No | SUB | 31.0 | 2014-08-09 | 2016.0 | 60.0 | 39.0 | 45.0 | 46.0 | 39.0 | 53.0 | 0.0 | NaN | 37 | 29 | 41 | 55 | 31 | 42 | 33 | 32 | 44 | 43 | 53 | 65 | 59 | 50 | 66 | 54 | 61 | 53 | 54 | 48 | 49 | 42 | 47 | 41 | 35 | 37 | 38 | 42 | 16 | 16 | 8 | 9 | 14 | 45 | 45 | 45 | 45 | 46 | 46 | 46 | 45 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 46 | 45 | 45 | 45 | 46 | 45 | 44 | 44 | 44 | 45 |
| 226099 | A. Bishop | 17 | 1998-06-12 | 182 | 71 | England | Mansfield Town | 46 | 53 | 30000 | GK | Right | 1 | 2 | 1 | Medium/Medium | Lean | No | RES | 39.0 | 2014-07-01 | 2021.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 31.0 | NaN | 11 | 13 | 13 | 22 | 16 | 12 | 17 | 12 | 15 | 20 | 35 | 28 | 33 | 39 | 35 | 21 | 50 | 28 | 33 | 15 | 19 | 17 | 11 | 30 | 19 | 11 | 11 | 15 | 51 | 43 | 43 | 41 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 215734 | E. Farrell | 17 | 1997-07-06 | 182 | 72 | Republic of Ireland | Drogheda United | 45 | 61 | 60000 | GK | Right | 1 | 2 | 1 | Medium/Medium | Lean | No | SUB | 40.0 | 2012-08-01 | 2020.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 44.0 | NaN | 7 | 13 | 7 | 26 | -3 | 9 | 11 | -1 | 25 | 25 | 46 | 43 | 33 | 46 | 51 | 9 | 48 | 45 | 52 | -3 | 35 | 13 | 9 | -3 | 23 | 5 | -1 | -1 | 46 | 45 | 46 | 45 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 224866 | J. Akintunde | 19 | 1996-03-29 | 175 | 75 | England | Cambridge United | 45 | 57 | 60000 | ST | Right | 1 | 3 | 2 | Medium/Medium | Stocky | No | RES | 26.0 | 2014-01-01 | 2021.0 | 70.0 | 46.0 | 37.0 | 40.0 | 17.0 | 47.0 | 0.0 | NaN | 28 | 45 | 35 | 41 | 36 | 38 | 37 | 29 | 32 | 27 | 71 | 70 | 57 | 23 | 57 | 39 | 60 | 59 | 46 | 46 | 32 | 15 | 33 | 44 | 44 | -1 | -1 | 7 | 13 | 14 | 10 | 8 | 10 | 45 | 45 | 45 | 44 | 44 | 44 | 44 | 44 | 43 | 43 | 43 | 43 | 38 | 38 | 38 | 43 | 34 | 30 | 30 | 30 | 34 | 33 | 28 | 28 | 28 | 33 |
| 224868 | M. Lowe | 19 | 1996-03-11 | 179 | 74 | England | Cambridge United | 45 | 55 | 50000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 27.0 | 2014-01-01 | 2021.0 | 69.0 | 43.0 | 49.0 | 48.0 | 36.0 | 44.0 | 0.0 | NaN | 43 | 45 | 36 | 56 | 43 | 48 | 41 | 37 | 55 | 38 | 68 | 69 | 74 | 38 | 64 | 46 | 58 | 48 | 46 | 34 | 28 | 27 | 47 | 43 | 44 | 36 | 37 | 47 | 11 | 6 | 9 | 6 | 13 | 46 | 46 | 46 | 48 | 47 | 47 | 47 | 48 | 48 | 48 | 48 | 49 | 45 | 45 | 45 | 49 | 44 | 41 | 41 | 41 | 44 | 44 | 39 | 39 | 39 | 44 |
| 228582 | S. Sargeant | 17 | 1997-09-23 | 183 | 67 | England | Leyton Orient | 44 | 63 | 60000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Lean | No | RES | 31.0 | 2014-09-01 | 2016.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 44.0 | NaN | 13 | 11 | 14 | 24 | 16 | 17 | 16 | 13 | 24 | 17 | 49 | 40 | 32 | 29 | 60 | 24 | 51 | 26 | 33 | 13 | 18 | 18 | 16 | 18 | 24 | 16 | 15 | 19 | 46 | 42 | 43 | 45 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 225873 | R. Feely | 18 | 1997-01-03 | 171 | 66 | Republic of Ireland | St. Patrick's Athletic | 44 | 61 | 60000 | CM | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | SUB | 30.0 | 2014-08-01 | 2015.0 | 70.0 | 38.0 | 41.0 | 48.0 | 40.0 | 42.0 | 0.0 | NaN | 44 | 40 | 40 | 35 | 34 | 51 | 37 | 35 | 45 | 50 | 75 | 74 | 54 | 35 | 52 | 39 | 49 | 73 | 30 | 37 | 38 | 40 | 42 | 47 | 42 | 40 | 43 | 37 | 11 | 10 | 14 | 9 | 17 | 42 | 42 | 42 | 46 | 45 | 45 | 45 | 46 | 45 | 45 | 45 | 48 | 44 | 44 | 44 | 48 | 46 | 42 | 42 | 42 | 46 | 46 | 39 | 39 | 39 | 46 |
| 227180 | A. O'Kelly | 17 | 1997-08-13 | 180 | 72 | England | Newport County | 44 | 60 | 60000 | RWB | Right | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 50.0 | 2015-01-20 | 2016.0 | 65.0 | 22.0 | 33.0 | 37.0 | 43.0 | 50.0 | 0.0 | NaN | 48 | 13 | 41 | 42 | 28 | 11 | 30 | 27 | 13 | 37 | 63 | 66 | 71 | 17 | 78 | 15 | 52 | 55 | 74 | 15 | 9 | 44 | 40 | 28 | 35 | 49 | 52 | 51 | 12 | 16 | 9 | 8 | 9 | 34 | 34 | 34 | 37 | 34 | 34 | 34 | 37 | 34 | 34 | 34 | 37 | 32 | 32 | 32 | 37 | 44 | 38 | 38 | 38 | 44 | 45 | 43 | 43 | 43 | 45 |
| 227244 | D. Clifton | 18 | 1996-11-08 | 178 | 66 | England | Northampton Town | 44 | 60 | 60000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 28.0 | 2015-01-24 | 2016.0 | 64.0 | 32.0 | 49.0 | 48.0 | 44.0 | 46.0 | 0.0 | NaN | 49 | 29 | 49 | 57 | 33 | 43 | 34 | 34 | 50 | 43 | 60 | 68 | 63 | 19 | 73 | 46 | 63 | 43 | 42 | 27 | 54 | 40 | 28 | 47 | 45 | 45 | 44 | 47 | 8 | 13 | 11 | 17 | 8 | 40 | 40 | 40 | 44 | 42 | 42 | 42 | 44 | 44 | 44 | 44 | 46 | 44 | 44 | 44 | 46 | 47 | 45 | 45 | 45 | 47 | 47 | 46 | 46 | 46 | 47 |
| 227881 | S. McWilliams | 16 | 1998-08-14 | 180 | 69 | England | Northampton Town | 44 | 59 | 60000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Normal | No | RES | 30.0 | 2015-02-21 | 2021.0 | 67.0 | 41.0 | 45.0 | 50.0 | 35.0 | 46.0 | 0.0 | NaN | 41 | 39 | 36 | 47 | 38 | 49 | 42 | 37 | 49 | 48 | 65 | 68 | 59 | 37 | 68 | 50 | 50 | 48 | 47 | 37 | 39 | 30 | 34 | 46 | 42 | 34 | 36 | 40 | 6 | 14 | 16 | 13 | 12 | 44 | 44 | 44 | 47 | 45 | 45 | 45 | 47 | 46 | 46 | 46 | 47 | 44 | 44 | 44 | 47 | 43 | 41 | 41 | 41 | 43 | 42 | 39 | 39 | 39 | 42 |
| 224867 | R. Horne | 19 | 1995-11-02 | 180 | 76 | England | Cambridge United | 44 | 57 | 60000 | CM | Right | 1 | 3 | 2 | Medium/Medium | Lean | No | RES | 28.0 | 2014-01-01 | 2021.0 | 63.0 | 40.0 | 43.0 | 47.0 | 39.0 | 52.0 | 0.0 | NaN | 40 | 41 | 40 | 36 | 41 | 45 | 43 | 42 | 37 | 39 | 67 | 60 | 57 | 25 | 54 | 39 | 48 | 55 | 65 | 40 | 30 | 36 | 48 | 44 | 29 | 38 | 46 | 42 | 10 | 14 | 9 | 12 | 9 | 44 | 44 | 44 | 46 | 45 | 45 | 45 | 46 | 45 | 45 | 45 | 46 | 44 | 44 | 44 | 46 | 44 | 42 | 42 | 42 | 44 | 43 | 42 | 42 | 42 | 43 |
| 227910 | L. Gooch | 17 | 1997-11-25 | 181 | 65 | England | Luton Town | 44 | 54 | 40000 | GK | Right | 1 | 3 | 1 | Medium/Medium | Lean | No | RES | 37.0 | 2015-02-21 | 2021.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 41.0 | NaN | 13 | 13 | 14 | 27 | 16 | 21 | 19 | 17 | 17 | 23 | 42 | 41 | 35 | 35 | 60 | 20 | 54 | 21 | 51 | 15 | 20 | 23 | 14 | 27 | 21 | 14 | 19 | 19 | 52 | 43 | 45 | 37 | 47 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 225339 | S. Warburton | 18 | 1996-10-10 | 171 | 64 | England | Northampton Town | 44 | 51 | 30000 | LB, LM | Left | 1 | 2 | 2 | Medium/Medium | Lean | No | RES | 29.0 | 2014-08-24 | 2016.0 | 58.0 | 28.0 | 32.0 | 45.0 | 43.0 | 45.0 | 0.0 | NaN | 41 | 27 | 40 | 27 | 29 | 39 | 33 | 29 | 28 | 36 | 62 | 55 | 57 | 29 | 70 | 29 | 84 | 62 | 26 | 27 | 48 | 43 | 39 | 40 | 39 | 43 | 45 | 46 | 12 | 13 | 11 | 13 | 16 | 36 | 36 | 36 | 40 | 38 | 38 | 38 | 40 | 37 | 37 | 37 | 41 | 36 | 36 | 36 | 41 | 44 | 39 | 39 | 39 | 44 | 44 | 42 | 42 | 42 | 44 |
| 11728 | B. Richardson | 45 | 1969-08-05 | 185 | 77 | England | Wycombe Wanderers | 44 | 44 | 10000 | GK | Right | 1 | 2 | 1 | Medium/Medium | Stocky | No | SUB | 13.0 | 2014-01-30 | 2021.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 25.0 | NaN | -3 | -3 | -1 | -1 | -1 | -3 | -1 | -3 | 1 | 23 | 25 | 25 | 38 | 34 | 44 | 1 | 51 | 32 | 47 | 7 | 45 | 7 | 1 | 9 | 5 | 3 | -1 | 1 | 37 | 55 | 37 | 59 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
10559 rows × 89 columns
draw_graphs(dataset=players_top_20_nations,title="Average player value per nationality",
analyzed_feature="value_eur",groupby_feature="nationality",
asc=True,xtitle="Value",ytitle="Nationality",
stat_function="mean",
orient='h',num_recs=20,marker_color="rebeccapurple")
Brazilian players have quite substantial prevailance in the price per player. Here interesting part is though the Netherlands have youngest players, their average value is quite high compared to other nationalities.
Now it is time to see where the top 300 most expensive players come from. This we will also visualize on a map. For this purpose we will import another dataset which contains the geographical coordinates of the capitals of countries. So, in this way we will position each player in his home country. Functions for spatial visualizations are taken from [3]
country_coords=pd.read_csv("data/gps_coords.csv")
country_coords.head()
| CountryName | CapitalName | CapitalLatitude | CapitalLongitude | CountryCode | ContinentName | |
|---|---|---|---|---|---|---|
| 0 | Somaliland | Hargeisa | 9.550000 | 44.050000 | NaN | Africa |
| 1 | South Georgia and South Sandwich Islands | King Edward Point | -54.283333 | -36.500000 | GS | Antarctica |
| 2 | French Southern and Antarctic Lands | Port-aux-Français | -49.350000 | 70.216667 | TF | Antarctica |
| 3 | Palestine | Jerusalem | 31.766667 | 35.233333 | PS | Asia |
| 4 | Aland Islands | Mariehamn | 60.116667 | 19.900000 | AX | Europe |
country_coords.describe()
| CapitalLatitude | CapitalLongitude | |
|---|---|---|
| count | 245.000000 | 245.000000 |
| mean | 17.335901 | 12.905790 |
| std | 25.739778 | 72.941443 |
| min | -54.283333 | -175.200000 |
| 25% | 0.316667 | -36.500000 |
| 50% | 16.700000 | 15.300000 |
| 75% | 38.883333 | 45.333333 |
| max | 78.216667 | 179.216667 |
country_coords.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 245 entries, 0 to 244 Data columns (total 6 columns): CountryName 245 non-null object CapitalName 241 non-null object CapitalLatitude 245 non-null float64 CapitalLongitude 245 non-null float64 CountryCode 242 non-null object ContinentName 245 non-null object dtypes: float64(2), object(4) memory usage: 11.6+ KB
From this analysis we can see that there is no missing data. The dataset has column for country, so we can directly join both datasets and take the coordinates.
top_300_val_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[:300]
top_300_val_players=pd.DataFrame(top_300_val_players,index=None).reset_index()
top_300_val_players=pd.DataFrame(top_300_val_players)
top_300_val_players.merge(country_coords, left_on='nationality', right_on='CountryName')
| short_name | nationality | value_eur | CountryName | CapitalName | CapitalLatitude | CapitalLongitude | CountryCode | ContinentName | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | L. Messi | Argentina | 111000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 1 | S. Agüero | Argentina | 47500000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 2 | J. Pastore | Argentina | 34500000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 3 | G. Higuaín | Argentina | 34500000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 4 | C. Tévez | Argentina | 34500000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 5 | A. Di María | Argentina | 34000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 6 | N. Gaitán | Argentina | 25500000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 7 | N. Otamendi | Argentina | 24000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 8 | R. Pereyra | Argentina | 23000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 9 | E. Garay | Argentina | 21000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 10 | M. Musacchio | Argentina | 20000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 11 | E. Banega | Argentina | 20000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 12 | E. Salvio | Argentina | 18000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 13 | P. Piatti | Argentina | 17500000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 14 | M. Rojo | Argentina | 17500000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 15 | L. Biglia | Argentina | 15000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 16 | L. Vietto | Argentina | 15000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 17 | J. Mascherano | Argentina | 15000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 18 | M. Icardi | Argentina | 14500000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 19 | P. Zabaleta | Argentina | 14000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 20 | P. Dybala | Argentina | 14000000 | Argentina | Buenos Aires | -34.583333 | -58.666667 | AR | South America |
| 21 | Cristiano Ronaldo | Portugal | 85500000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 22 | João Moutinho | Portugal | 23000000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 23 | Nani | Portugal | 21000000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 24 | William Carvalho | Portugal | 20500000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 25 | André Gomes | Portugal | 20000000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 26 | A. Lopes | Portugal | 19500000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 27 | Danny | Portugal | 17000000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 28 | Adrien Silva | Portugal | 17000000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 29 | Pepe | Portugal | 17000000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 30 | Rui Patrício | Portugal | 16500000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 31 | Bernardo Silva | Portugal | 14000000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 32 | Quaresma | Portugal | 14000000 | Portugal | Lisbon | 38.716667 | -9.133333 | PT | Europe |
| 33 | E. Hazard | Belgium | 74000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 34 | K. De Bruyne | Belgium | 50500000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 35 | T. Courtois | Belgium | 44000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 36 | C. Benteke | Belgium | 27000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 37 | R. Nainggolan | Belgium | 26500000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 38 | V. Kompany | Belgium | 25000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 39 | D. Mertens | Belgium | 22000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 40 | A. Witsel | Belgium | 20000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 41 | R. Lukaku | Belgium | 20000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 42 | T. Alderweireld | Belgium | 17000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 43 | J. Vertonghen | Belgium | 16000000 | Belgium | Brussels | 50.833333 | 4.333333 | BE | Europe |
| 44 | Neymar | Brazil | 71500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 45 | Oscar | Brazil | 38000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 46 | Thiago Silva | Brazil | 38000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 47 | Coutinho | Brazil | 38000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 48 | Danilo | Brazil | 33400000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 49 | Marcelo | Brazil | 32700000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 50 | Hulk | Brazil | 28500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 51 | Maicon | Brazil | 27475000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 52 | Roberto Firmino | Brazil | 26500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 53 | Lucas | Brazil | 26000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 54 | Alex Teixeira | Brazil | 25000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 55 | Rafinha | Brazil | 23675000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 56 | Willian | Brazil | 23500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 57 | Alex Sandro | Brazil | 22000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 58 | Miranda | Brazil | 21725000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 59 | Naldo | Brazil | 21600000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 60 | Douglas Costa | Brazil | 21500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 61 | David Luiz | Brazil | 21000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 62 | Marquinhos | Brazil | 20000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 63 | Luiz Gustavo | Brazil | 19000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 64 | Diego Alves | Brazil | 19000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 65 | Allan | Brazil | 18000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 66 | Maurício | Brazil | 17600000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 67 | Jonas | Brazil | 17500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 68 | Dani Alves | Brazil | 16000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 69 | Leandro Castán | Brazil | 16000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 70 | Raffael | Brazil | 15500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 71 | Felipe Anderson | Brazil | 15500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 72 | Gabriel | Brazil | 14800000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 73 | Taison | Brazil | 14500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 74 | Filipe Luís | Brazil | 14500000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 75 | Rafael | Brazil | 14400000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 76 | Aderllan Santos | Brazil | 14000000 | Brazil | Brasilia | -15.783333 | -47.916667 | BR | South America |
| 77 | L. Suárez | Uruguay | 69000000 | Uruguay | Montevideo | -34.850000 | -56.166667 | UY | South America |
| 78 | E. Cavani | Uruguay | 33500000 | Uruguay | Montevideo | -34.850000 | -56.166667 | UY | South America |
| 79 | D. Godín | Uruguay | 25000000 | Uruguay | Montevideo | -34.850000 | -56.166667 | UY | South America |
| 80 | F. Muslera | Uruguay | 17000000 | Uruguay | Montevideo | -34.850000 | -56.166667 | UY | South America |
| 81 | J. Rodríguez | Colombia | 63020000 | Colombia | Bogota | 4.600000 | -74.083333 | CO | South America |
| 82 | J. Martínez | Colombia | 29800000 | Colombia | Bogota | 4.600000 | -74.083333 | CO | South America |
| 83 | Falcao | Colombia | 23000000 | Colombia | Bogota | 4.600000 | -74.083333 | CO | South America |
| 84 | J. Cuadrado | Colombia | 21700000 | Colombia | Bogota | 4.600000 | -74.083333 | CO | South America |
| 85 | C. Bacca | Colombia | 20500000 | Colombia | Bogota | 4.600000 | -74.083333 | CO | South America |
| 86 | M. Neuer | Germany | 58000000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 87 | T. Kroos | Germany | 54500000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 88 | M. Özil | Germany | 52500000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 89 | T. Müller | Germany | 47500000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 90 | M. Reus | Germany | 45500000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 91 | J. Boateng | Germany | 45000000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 92 | M. Hummels | Germany | 39000000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 93 | M. Götze | Germany | 38000000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 94 | B. Schweinsteiger | Germany | 35000000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 95 | B. Leno | Germany | 32500000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 96 | P. Lahm | Germany | 29500000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 97 | K. Bellarabi | Germany | 27500000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 98 | I. Gündoğan | Germany | 26000000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| 99 | M. ter Stegen | Germany | 23500000 | Germany | Berlin | 52.516667 | 13.400000 | DE | Europe |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 175 | Bojan | Spain | 21500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 176 | Paco Alcácer | Spain | 21500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 177 | Vitolo | Spain | 21375000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 178 | Ander Herrera | Spain | 20500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 179 | Nolito | Spain | 20000000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 180 | Azpilicueta | Spain | 19500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 181 | Muniaín | Spain | 19500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 182 | Casillas | Spain | 19000000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 183 | Carvajal | Spain | 18500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 184 | Camacho | Spain | 18025000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 185 | Borja Valero | Spain | 18000000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 186 | Gayà | Spain | 17000000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 187 | José Callejón | Spain | 16500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 188 | Negredo | Spain | 16500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 189 | Iturraspe | Spain | 16500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 190 | Marc Bartra | Spain | 16500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 191 | Iborra | Spain | 16000000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 192 | Iñigo Martínez | Spain | 16000000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 193 | Óliver Torres | Spain | 15500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 194 | Víctor Ruíz | Spain | 15000000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 195 | Juanfran | Spain | 14800000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 196 | Sergio Asenjo | Spain | 14500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 197 | De Marcos | Spain | 14500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 198 | Mikel San José | Spain | 14500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 199 | Victor Valdés | Spain | 14500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 200 | Diego López | Spain | 14500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 201 | Kiko Casilla | Spain | 14500000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 202 | Rodrigo | Spain | 14000000 | Spain | Madrid | 40.400000 | -3.683333 | ES | Europe |
| 203 | A. Sánchez | Chile | 47000000 | Chile | Santiago | -33.450000 | -70.666667 | CL | South America |
| 204 | A. Vidal | Chile | 37500000 | Chile | Santiago | -33.450000 | -70.666667 | CL | South America |
| 205 | C. Bravo | Chile | 18475000 | Chile | Santiago | -33.450000 | -70.666667 | CL | South America |
| 206 | G. Medel | Chile | 15500000 | Chile | Santiago | -33.450000 | -70.666667 | CL | South America |
| 207 | L. Modrić | Croatia | 41500000 | Croatia | Zagreb | 45.800000 | 16.000000 | HR | Europe |
| 208 | I. Rakitić | Croatia | 31500000 | Croatia | Zagreb | 45.800000 | 16.000000 | HR | Europe |
| 209 | M. Mandžukić | Croatia | 23000000 | Croatia | Zagreb | 45.800000 | 16.000000 | HR | Europe |
| 210 | D. Subašić | Croatia | 14000000 | Croatia | Zagreb | 45.800000 | 16.000000 | HR | Europe |
| 211 | Z. Ibrahimović | Sweden | 40500000 | Sweden | Stockholm | 59.333333 | 18.050000 | SE | Europe |
| 212 | M. Verratti | Italy | 38000000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 213 | G. Chiellini | Italy | 32500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 214 | C. Marchisio | Italy | 26500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 215 | L. Insigne | Italy | 23000000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 216 | A. Candreva | Italy | 23000000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 217 | M. Perin | Italy | 20500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 218 | S. El Shaarawy | Italy | 20000000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 219 | D. Berardi | Italy | 19500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 220 | M. Balotelli | Italy | 18500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 221 | L. Bonucci | Italy | 18500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 222 | M. Darmian | Italy | 17500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 223 | S. Sirigu | Italy | 17500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 224 | S. Giovinco | Italy | 16500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 225 | M. Parolo | Italy | 15000000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 226 | D. De Rossi | Italy | 15000000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 227 | G. Rossi | Italy | 14500000 | Italy | Rome | 41.900000 | 12.483333 | IT | Europe |
| 228 | D. Alaba | Austria | 35500000 | Austria | Vienna | 48.200000 | 16.366667 | AT | Europe |
| 229 | C. Eriksen | Denmark | 31000000 | Denmark | Copenhagen | 55.666667 | 12.583333 | DK | Europe |
| 230 | S. Kjær | Denmark | 18000000 | Denmark | Copenhagen | 55.666667 | 12.583333 | DK | Europe |
| 231 | N. Matić | Serbia | 29500000 | Serbia | Belgrade | 44.833333 | 20.500000 | RS | Europe |
| 232 | F. Djordjevic | Serbia | 17000000 | Serbia | Belgrade | 44.833333 | 20.500000 | RS | Europe |
| 233 | N. Maksimović | Serbia | 17000000 | Serbia | Belgrade | 44.833333 | 20.500000 | RS | Europe |
| 234 | N. Subotić | Serbia | 14000000 | Serbia | Belgrade | 44.833333 | 20.500000 | RS | Europe |
| 235 | A. Ljajić | Serbia | 14000000 | Serbia | Belgrade | 44.833333 | 20.500000 | RS | Europe |
| 236 | A. Turan | Turkey | 26950000 | Turkey | Ankara | 39.933333 | 32.866667 | TR | Europe |
| 237 | N. Şahin | Turkey | 19500000 | Turkey | Ankara | 39.933333 | 32.866667 | TR | Europe |
| 238 | G. Töre | Turkey | 19000000 | Turkey | Ankara | 39.933333 | 32.866667 | TR | Europe |
| 239 | O. Toprak | Turkey | 17000000 | Turkey | Ankara | 39.933333 | 32.866667 | TR | Europe |
| 240 | R. Rodriguez | Switzerland | 26600000 | Switzerland | Bern | 46.916667 | 7.466667 | CH | Europe |
| 241 | G. Xhaka | Switzerland | 21500000 | Switzerland | Bern | 46.916667 | 7.466667 | CH | Europe |
| 242 | Y. Sommer | Switzerland | 20000000 | Switzerland | Bern | 46.916667 | 7.466667 | CH | Europe |
| 243 | X. Shaqiri | Switzerland | 18500000 | Switzerland | Bern | 46.916667 | 7.466667 | CH | Europe |
| 244 | R. Bürki | Switzerland | 16000000 | Switzerland | Bern | 46.916667 | 7.466667 | CH | Europe |
| 245 | M. Hamšik | Slovakia | 26500000 | Slovakia | Bratislava | 48.150000 | 17.116667 | SK | Europe |
| 246 | Y. Konoplyanka | Ukraine | 25500000 | Ukraine | Kyiv | 50.433333 | 30.516667 | UA | Europe |
| 247 | C. Vela | Mexico | 24500000 | Mexico | Mexico City | 19.433333 | -99.133333 | MX | Central America |
| 248 | J. Hernández | Mexico | 16850000 | Mexico | Mexico City | 19.433333 | -99.133333 | MX | Central America |
| 249 | P. Aubameyang | Gabon | 24500000 | Gabon | Libreville | 0.383333 | 9.450000 | GA | Africa |
| 250 | P. Čech | Czech Republic | 24000000 | Czech Republic | Prague | 50.083333 | 14.466667 | CZ | Europe |
| 251 | S. Kagawa | Japan | 23500000 | Japan | Tokyo | 35.683333 | 139.750000 | JP | Asia |
| 252 | M. Benatia | Morocco | 23000000 | Morocco | Rabat | 34.016667 | -6.816667 | MA | Africa |
| 253 | K. Manolas | Greece | 22500000 | Greece | Athens | 37.983333 | 23.733333 | GR | Europe |
| 254 | Sokratis | Greece | 21500000 | Greece | Athens | 37.983333 | 23.733333 | GR | Europe |
| 255 | A. Samaris | Greece | 15000000 | Greece | Athens | 37.983333 | 23.733333 | GR | Europe |
| 256 | R. Eremenko | Finland | 22000000 | Finland | Helsinki | 60.166667 | 24.933333 | FI | Europe |
| 257 | S. Feghouli | Algeria | 21500000 | Algeria | Algiers | 36.750000 | 3.050000 | DZ | Africa |
| 258 | Y. Brahimi | Algeria | 18000000 | Algeria | Algiers | 36.750000 | 3.050000 | DZ | Africa |
| 259 | M. Salah | Egypt | 20500000 | Egypt | Cairo | 30.050000 | 31.250000 | EG | Africa |
| 260 | S. Jovetić | Montenegro | 20500000 | Montenegro | Podgorica | 42.433333 | 19.266667 | ME | Europe |
| 261 | S. Savić | Montenegro | 18500000 | Montenegro | Podgorica | 42.433333 | 19.266667 | ME | Europe |
| 262 | H. Mkhitaryan | Armenia | 19500000 | Armenia | Yerevan | 40.166667 | 44.500000 | AM | Europe |
| 263 | J. Oblak | Slovenia | 19500000 | Slovenia | Ljubljana | 46.050000 | 14.516667 | SI | Europe |
| 264 | S. Handanovič | Slovenia | 19000000 | Slovenia | Ljubljana | 46.050000 | 14.516667 | SI | Europe |
| 265 | A. Carrillo | Peru | 19000000 | Peru | Lima | -12.050000 | -77.050000 | PE | South America |
| 266 | N. Nkoulou | Cameroon | 18500000 | Cameroon | Yaounde | 3.866667 | 11.516667 | CM | Africa |
| 267 | J. Matip | Cameroon | 15500000 | Cameroon | Yaounde | 3.866667 | 11.516667 | CM | Africa |
| 268 | V. Aboubakar | Cameroon | 14000000 | Cameroon | Yaounde | 3.866667 | 11.516667 | CM | Africa |
| 269 | O. Shatov | Russia | 18500000 | Russia | Moscow | 55.750000 | 37.600000 | RU | Europe |
| 270 | I. Smolnikov | Russia | 14500000 | Russia | Moscow | 55.750000 | 37.600000 | RU | Europe |
| 271 | I. Akinfeev | Russia | 14500000 | Russia | Moscow | 55.750000 | 37.600000 | RU | Europe |
| 272 | S. Rondón | Venezuela | 18000000 | Venezuela | Caracas | 10.483333 | -66.866667 | VE | South America |
| 273 | M. Bradley | United States | 15500000 | United States | Washington | 38.883333 | -77.000000 | US | Central America |
| 274 | B. Natcho | Israel | 15500000 | Israel | Jerusalem | 31.766667 | 35.233333 | IL | Asia |
275 rows × 9 columns
Though we chose 300 players, in the resulting merged dataset we can see only 275 records. This might mean that some of the countries' name do not coinside in both tables. Let's explore.
top_300_val_players.nationality.unique()
array(['Argentina', 'Portugal', 'Belgium', 'Brazil', 'Uruguay',
'Colombia', 'Germany', 'Netherlands', 'Wales', 'Poland', 'France',
'Spain', 'Chile', 'Croatia', 'Sweden', 'England', 'Italy',
'Austria', 'Bosnia Herzegovina', 'Denmark', 'Serbia', 'Turkey',
'Switzerland', 'Slovakia', 'Ukraine', 'Ivory Coast', 'Mexico',
'Gabon', 'Czech Republic', 'Japan', 'Morocco', 'Greece', 'Finland',
'Algeria', 'Egypt', 'Montenegro', 'Armenia', 'Slovenia', 'Peru',
'Cameroon', 'Russia', 'Venezuela', 'Republic of Ireland',
'United States', 'Israel'], dtype=object)
country_coords.CountryName.unique()
array(['Somaliland', 'South Georgia and South Sandwich Islands',
'French Southern and Antarctic Lands', 'Palestine',
'Aland Islands', 'Nauru', 'Saint Martin', 'Tokelau',
'Western Sahara', 'Afghanistan', 'Albania', 'Algeria',
'American Samoa', 'Andorra', 'Angola', 'Anguilla',
'Antigua and Barbuda', 'Argentina', 'Armenia', 'Aruba',
'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',
'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',
'Bermuda', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina',
'Botswana', 'Brazil', 'British Virgin Islands',
'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Myanmar',
'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde',
'Cayman Islands', 'Central African Republic', 'Chad', 'Chile',
'China', 'Christmas Island', 'Cocos Islands', 'Colombia',
'Comoros', 'Democratic Republic of the Congo', 'Republic of Congo',
'Cook Islands', 'Costa Rica', "Cote d'Ivoire", 'Croatia', 'Cuba',
'Curaçao', 'Cyprus', 'Czech Republic', 'Denmark', 'Djibouti',
'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',
'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',
'Ethiopia', 'Falkland Islands', 'Faroe Islands', 'Fiji', 'Finland',
'France', 'French Polynesia', 'Gabon', 'The Gambia', 'Georgia',
'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Grenada',
'Guam', 'Guatemala', 'Guernsey', 'Guinea', 'Guinea-Bissau',
'Guyana', 'Haiti', 'Vatican City', 'Honduras', 'Hungary',
'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland',
'Isle of Man', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jersey',
'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'North Korea',
'South Korea', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',
'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein',
'Lithuania', 'Luxembourg', 'Macedonia', 'Madagascar', 'Malawi',
'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands',
'Mauritania', 'Mauritius', 'Mexico',
'Federated States of Micronesia', 'Moldova', 'Monaco', 'Mongolia',
'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Namibia',
'Nepal', 'Netherlands', 'New Caledonia', 'New Zealand',
'Nicaragua', 'Niger', 'Nigeria', 'Niue', 'Norfolk Island',
'Northern Mariana Islands', 'Norway', 'Oman', 'Pakistan', 'Palau',
'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines',
'Pitcairn Islands', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',
'Romania', 'Russia', 'Rwanda', 'Saint Barthelemy', 'Saint Helena',
'Saint Kitts and Nevis', 'Saint Lucia',
'Saint Pierre and Miquelon', 'Saint Vincent and the Grenadines',
'Samoa', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia',
'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',
'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',
'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka',
'Sudan', 'Suriname', 'Svalbard', 'Swaziland', 'Sweden',
'Switzerland', 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania',
'Thailand', 'Timor-Leste', 'Togo', 'Tonga', 'Trinidad and Tobago',
'Tunisia', 'Turkey', 'Turkmenistan', 'Turks and Caicos Islands',
'Tuvalu', 'Uganda', 'Ukraine', 'United Arab Emirates',
'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',
'Vanuatu', 'Venezuela', 'Vietnam', 'US Virgin Islands',
'Wallis and Futuna', 'Yemen', 'Zambia', 'Zimbabwe',
'US Minor Outlying Islands', 'Antarctica', 'Northern Cyprus',
'Hong Kong', 'Heard Island and McDonald Islands',
'British Indian Ocean Territory', 'Macau'], dtype=object)
top_300_set=set(top_300_val_players.nationality.unique())
country_set=set(country_coords.CountryName.unique())
top_300_set.issubset(country_set)
False
False shows that our suspicion for not coinciding names is true and by subtracting two sets we can identify the differences.
top_300_set-country_set
{'Bosnia Herzegovina',
'England',
'Ivory Coast',
'Republic of Ireland',
'Wales'}
Let's see how these countries' names are spelt in the country_set. And here is the difference: "Bosnia Herzegovina" is instead of 'Bosnia and Herzegovina' and "Cote d'Ivoire" stands for "Ivory coast". England and Wales do not exist in the database as separate countries, which is logical. Let's change the names of "Bosnia and Herzegovina", "Cote d'Ivoire" and "Ireland" (in the dataset the coordinates are thos of Dublin, so we should change it to Republic of Ireland). For England, Northern Ireland and Wales we will add records by using a dictionary.
country_coords.CountryName=country_coords.CountryName.str.replace("Bosnia and Herzegovina","Bosnia Herzegovina")
country_coords.CountryName=country_coords.CountryName.str.replace("Cote d'Ivoire","Ivory Coast")
country_coords.CountryName=country_coords.CountryName.str.replace("Ireland","Republic of Ireland")
coords_dict={"eng":{"CountryName":"England",
"CapitalName":"London",
"CapitalLatitude":"51.500000",
"CapitalLongitude":"-0.083333",
"CountryCode":"NaN",
"ContinentName":"Europe"},
"wal":{"CountryName":"Wales",
"CapitalName":"Cardiff",
"CapitalLatitude":"51.481583",
"CapitalLongitude":"-3.179090",
"CountryCode":"NaN",
"ContinentName":"Europe"},
"irl":{"CountryName":"Northern Ireland",
"CapitalName":"Belfast",
"CapitalLatitude":" 54.607868",
"CapitalLongitude":"-5.926437",
"CountryCode":"NaN",
"ContinentName":"Europe"}}
for key,value in coords_dict.items():
country_coords=country_coords.append(coords_dict[key],ignore_index=True)
Now, let's do the merge again.
players_coordinates=top_300_val_players.merge(country_coords, left_on='nationality', right_on='CountryName')
players_coordinates.shape
(300, 9)
This time the records are exactly 300.
players_coordinates.CapitalLongitude=players_coordinates.CapitalLongitude.astype(float)
players_coordinates.CapitalLatitude=players_coordinates.CapitalLatitude.astype(float)
plt.figure(figsize = (12, 10))
m = Basemap(projection = "merc", llcrnrlat = -73, llcrnrlon = -180, urcrnrlat = 80, urcrnrlon = 180)
x, y = None, None
x, y = m(players_coordinates.CapitalLongitude.tolist(),players_coordinates.CapitalLatitude.tolist())
m.plot(x,y,"o",color="red",markersize=2)
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color = "lightgreen", lake_color = "aqua")
m.drawmapboundary(fill_color = "aqua")
plt.show()
C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:2: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
def plot_players_densities(coord_data, title = "Players densities"):
plt.figure(figsize = (15, 10))
m = Basemap(projection = "merc", llcrnrlat = -73, llcrnrlon = -180, urcrnrlat = 80, urcrnrlon = 180)
# Prepare histogram bins
num_bins = 100
lon_bins = np.linspace(-180, 180, num_bins + 1)
lat_bins = np.linspace(-90, 90, num_bins + 1)
# Create 2D histogram values
density, x_breaks, y_breaks = np.histogram2d(
coord_data.CapitalLatitude,
coord_data.CapitalLongitude,
[lat_bins, lon_bins])
# Create the basis of the histogram - the (x, y) value pairs
# and map them to 2D distances
lon_bins_2d, lat_bins_2d = np.meshgrid(lon_bins, lat_bins)
x, y = m(lon_bins_2d, lat_bins_2d)
m.drawcoastlines()
m.drawcountries()
m.pcolormesh(x, y, density)
m.colorbar()
plt.title(title)
plt.show()
plot_players_densities(players_coordinates)
C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:3: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:22: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
Most of the 300 highest paid professionals are from Spain. We will investigate little bit by checking what si their number in the second group of 300 players.
second_300_val_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[301:600]
second_300_val_players=pd.DataFrame(second_300_val_players,index=None).reset_index()
second_300_val_players.groupby("nationality")["nationality"].count().sort_values(ascending=False).head(10)
nationality Spain 43 France 25 Brazil 23 Italy 20 England 19 Germany 16 Argentina 14 Portugal 14 Netherlands 11 Turkey 10 Name: nationality, dtype: int64
Again we can see that spanish players are the biggest number in this group. Though the country with highest number of representatives in the database is England (about 47% more).
And now let's see the lower range players. Will take players from 8001-st to 8300-th in the descending list of player values.
lower_paid_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[8001:8300]
lower_paid_players=pd.DataFrame(lower_paid_players,index=None).reset_index()
lower_paid_players.groupby("nationality")["nationality"].count().sort_values(ascending=False).head(10)
nationality England 23 France 21 Argentina 20 United States 18 Spain 15 Italy 15 Chile 15 Germany 12 Poland 11 Colombia 11 Name: nationality, dtype: int64
Here we can see that Spaniards are not largest in number.
From these last few statistics it seems that, in general, Spanish players are better paid than others, though the average price per player is not highest. Still, number of players in the set is very high and this can be the reason for this result.
nationality_and_value=players_top_20_nations[["nationality","value_eur"]]
fig = px.box(nationality_and_value, x="nationality", y="value_eur",title="Value per Nation")
fig.show()
players_top_20_nations[players_top_20_nations["nationality"]=="Spain"]["value_eur"].median()
875000.0
players_top_20_nations[players_top_20_nations["nationality"]=="England"]["value_eur"].median()
300000.0
Seeing the median values for Brazil(1.8M), Portugal(1.2M), Spain(875000) and England(300000) and having in mind the number of players from Spain (987), England (1450), Brazil(420) and Portugal(324), we can only conclude that Spanish players are much better paid than English ones. Brazil and Portugal are less in numbers and each have 1 very expensive player which rizes the average value per nationality.
Lets make a hypothesis:
$H0$ - there is not a substantial difference in the payment of English and Spanish players
$H1$ - there is a substantial difference in the payment of English and Spanish players
and we will use $\alpha_{c}$=1% since we have big dataset.
This we will test now and verify with the second dataset for 2020.
english_players_price=players_16[players_16["nationality"]=="England"]["value_eur"]
spanish_players_price=players_16[players_16["nationality"]=="Spain"]["value_eur"]
ttest_ind(english_players_price, spanish_players_price)
Ttest_indResult(statistic=-11.46332634242886, pvalue=1.1461395477178186e-29)
With such a low pvalue we can reject the Null Hypothesis.
The final aim of this work is to create and train a model to predict the price of a player based on his capabilities. Before that we can see which features are most correlated to the value.
players_16.corr()
| age | height_cm | weight_kg | overall | potential | value_eur | international_reputation | weak_foot | skill_moves | team_jersey_number | contract_valid_until | pace | shooting | passing | dribbling | defending | physic | gk_speed | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1.000000 | 0.080412 | 0.218434 | 0.439736 | -0.125627 | 0.079883 | 0.277710 | 0.077216 | -0.018800 | -0.228214 | -0.151552 | -0.164913 | 0.048660 | 0.062909 | -0.026930 | 0.100032 | 0.069155 | 0.086077 | 0.062796 | 0.005349 | 0.080368 | 0.086728 | 0.061369 | -0.051007 | 0.070079 | 0.122131 | 0.139071 | 0.029144 | -0.224824 | -0.218302 | -0.073392 | 0.395858 | -0.123581 | 0.088910 | 0.121634 | 0.023215 | 0.259827 | 0.076456 | 0.237996 | 0.167387 | 0.014522 | 0.160532 | 0.071897 | 0.095487 | 0.085014 | 0.068744 | 0.110753 | 0.118913 | 0.114074 | 0.126850 | 0.109512 | 0.022931 | 0.022931 | 0.022931 | -0.003591 | 0.009109 | 0.009109 | 0.009109 | -0.003591 | 0.013235 | 0.013235 | 0.013235 | 0.001210 | 0.050825 | 0.050825 | 0.050825 | 0.001210 | 0.046002 | 0.084582 | 0.084582 | 0.084582 | 0.046002 | 0.049920 | 0.091531 | 0.091531 | 0.091531 | 0.049920 |
| height_cm | 0.080412 | 1.000000 | 0.766455 | 0.060498 | 0.017680 | 0.015859 | 0.045723 | -0.164617 | -0.413659 | -0.043021 | -0.094536 | -0.478165 | -0.378375 | -0.425457 | -0.481723 | -0.094135 | -0.137255 | 0.337328 | -0.458348 | -0.358111 | -0.028599 | -0.341943 | -0.344828 | -0.475107 | -0.421572 | -0.384103 | -0.319234 | -0.394236 | -0.515279 | -0.435221 | -0.599136 | 0.003806 | -0.787934 | -0.281572 | -0.073728 | -0.259871 | 0.531675 | -0.379441 | -0.045248 | -0.038329 | -0.414091 | -0.307677 | -0.320547 | -0.044097 | -0.052524 | -0.071170 | 0.345080 | 0.344673 | 0.340714 | 0.346370 | 0.343593 | -0.371797 | -0.371797 | -0.371797 | -0.455301 | -0.426990 | -0.426990 | -0.426990 | -0.455301 | -0.438802 | -0.438802 | -0.438802 | -0.445813 | -0.391435 | -0.391435 | -0.391435 | -0.445813 | -0.325603 | -0.258504 | -0.258504 | -0.258504 | -0.325603 | -0.285420 | -0.152169 | -0.152169 | -0.152169 | -0.285420 |
| weight_kg | 0.218434 | 0.766455 | 1.000000 | 0.134976 | 0.007411 | 0.042073 | 0.093912 | -0.129611 | -0.363400 | -0.084685 | -0.078132 | -0.451028 | -0.320896 | -0.379390 | -0.434535 | -0.094449 | -0.100458 | 0.323379 | -0.405305 | -0.312001 | -0.033478 | -0.302296 | -0.291449 | -0.431251 | -0.368087 | -0.326886 | -0.279506 | -0.353959 | -0.484357 | -0.413137 | -0.546209 | 0.071590 | -0.697291 | -0.220455 | -0.024147 | -0.231913 | 0.595008 | -0.316240 | -0.002919 | -0.035367 | -0.360297 | -0.253481 | -0.265101 | -0.051303 | -0.063893 | -0.081469 | 0.337671 | 0.339249 | 0.334518 | 0.340144 | 0.334678 | -0.325837 | -0.325837 | -0.325837 | -0.407746 | -0.379890 | -0.379890 | -0.379890 | -0.407746 | -0.392627 | -0.392627 | -0.392627 | -0.400793 | -0.350573 | -0.350573 | -0.350573 | -0.400793 | -0.298411 | -0.233983 | -0.233983 | -0.233983 | -0.298411 | -0.264230 | -0.137809 | -0.137809 | -0.137809 | -0.264230 |
| overall | 0.439736 | 0.060498 | 0.134976 | 1.000000 | 0.781817 | 0.598158 | 0.491618 | 0.205962 | 0.243675 | -0.179027 | -0.013182 | 0.144062 | 0.314795 | 0.356585 | 0.300196 | 0.250692 | 0.251938 | -0.050719 | 0.337289 | 0.258967 | 0.285335 | 0.462311 | 0.313773 | 0.301068 | 0.354916 | 0.329594 | 0.449014 | 0.406070 | 0.157273 | 0.176556 | 0.211860 | 0.792814 | 0.037695 | 0.376679 | 0.237238 | 0.303921 | 0.292941 | 0.341751 | 0.391474 | 0.306587 | 0.286826 | 0.454645 | 0.288142 | 0.199397 | 0.224607 | 0.191321 | -0.013305 | -0.008285 | -0.011916 | -0.004463 | -0.013170 | 0.321237 | 0.321237 | 0.321237 | 0.314071 | 0.324193 | 0.324193 | 0.324193 | 0.314071 | 0.328978 | 0.328978 | 0.328978 | 0.319067 | 0.355663 | 0.355663 | 0.355663 | 0.319067 | 0.314825 | 0.331386 | 0.331386 | 0.331386 | 0.314825 | 0.302586 | 0.287742 | 0.287742 | 0.287742 | 0.302586 |
| potential | -0.125627 | 0.017680 | 0.007411 | 0.781817 | 1.000000 | 0.576793 | 0.380552 | 0.178361 | 0.255135 | -0.022922 | 0.085902 | 0.213147 | 0.282428 | 0.314101 | 0.308350 | 0.177525 | 0.182002 | -0.080463 | 0.289771 | 0.255918 | 0.225003 | 0.419149 | 0.279155 | 0.328817 | 0.316465 | 0.261308 | 0.374957 | 0.391883 | 0.265961 | 0.278954 | 0.246533 | 0.590323 | 0.109251 | 0.322904 | 0.160963 | 0.246595 | 0.128617 | 0.296538 | 0.241544 | 0.199210 | 0.272169 | 0.380165 | 0.251420 | 0.129611 | 0.162444 | 0.139049 | -0.054610 | -0.052694 | -0.054882 | -0.053324 | -0.053684 | 0.297491 | 0.297491 | 0.297491 | 0.306260 | 0.310361 | 0.310361 | 0.310361 | 0.306260 | 0.313618 | 0.313618 | 0.313618 | 0.306790 | 0.317066 | 0.317066 | 0.317066 | 0.306790 | 0.270101 | 0.267885 | 0.267885 | 0.267885 | 0.270101 | 0.255058 | 0.217449 | 0.217449 | 0.217449 | 0.255058 |
| value_eur | 0.079883 | 0.015859 | 0.042073 | 0.598158 | 0.576793 | 1.000000 | 0.676484 | 0.140724 | 0.217407 | -0.065684 | 0.092588 | 0.106889 | 0.212288 | 0.214805 | 0.194875 | 0.086131 | 0.107833 | -0.015566 | 0.195654 | 0.194837 | 0.135334 | 0.272554 | 0.229246 | 0.204294 | 0.226008 | 0.204509 | 0.253297 | 0.245157 | 0.152016 | 0.153989 | 0.159247 | 0.471799 | 0.065007 | 0.217239 | 0.110937 | 0.164818 | 0.101879 | 0.216411 | 0.155696 | 0.114374 | 0.200393 | 0.294718 | 0.201538 | 0.043116 | 0.075388 | 0.056511 | -0.011160 | -0.010169 | -0.012252 | -0.011187 | -0.011180 | 0.206180 | 0.206180 | 0.206180 | 0.207746 | 0.213569 | 0.213569 | 0.213569 | 0.207746 | 0.214946 | 0.214946 | 0.214946 | 0.205865 | 0.214490 | 0.214490 | 0.214490 | 0.205865 | 0.164308 | 0.164751 | 0.164751 | 0.164751 | 0.164308 | 0.150920 | 0.123135 | 0.123135 | 0.123135 | 0.150920 |
| international_reputation | 0.277710 | 0.045723 | 0.093912 | 0.491618 | 0.380552 | 0.676484 | 1.000000 | 0.110302 | 0.138444 | -0.063367 | 0.002140 | 0.017002 | 0.160887 | 0.163882 | 0.123161 | 0.088488 | 0.080794 | 0.005066 | 0.142636 | 0.130259 | 0.124098 | 0.198796 | 0.192290 | 0.121119 | 0.173613 | 0.165791 | 0.197237 | 0.164953 | 0.016482 | 0.014446 | 0.056832 | 0.384748 | 0.004914 | 0.172561 | 0.093846 | 0.068915 | 0.112206 | 0.161859 | 0.145597 | 0.108258 | 0.135066 | 0.228296 | 0.181447 | 0.050547 | 0.073716 | 0.061860 | 0.008567 | 0.008862 | 0.007609 | 0.010341 | 0.007519 | 0.148768 | 0.148768 | 0.148768 | 0.139487 | 0.148277 | 0.148277 | 0.148277 | 0.139487 | 0.149761 | 0.149761 | 0.149761 | 0.138593 | 0.159101 | 0.159101 | 0.159101 | 0.138593 | 0.122380 | 0.134257 | 0.134257 | 0.134257 | 0.122380 | 0.115759 | 0.111357 | 0.111357 | 0.111357 | 0.115759 |
| weak_foot | 0.077216 | -0.164617 | -0.129611 | 0.205962 | 0.178361 | 0.140724 | 0.110302 | 1.000000 | 0.326654 | -0.043307 | -0.037653 | 0.269767 | 0.361478 | 0.332389 | 0.337112 | 0.088885 | 0.215682 | -0.233476 | 0.301782 | 0.342898 | 0.192889 | 0.306543 | 0.346696 | 0.333929 | 0.335910 | 0.321467 | 0.273411 | 0.338561 | 0.216583 | 0.205218 | 0.257929 | 0.175951 | 0.204510 | 0.314252 | 0.049819 | 0.204976 | -0.020808 | 0.347918 | 0.138597 | 0.056026 | 0.335953 | 0.302370 | 0.316899 | 0.025367 | 0.050568 | 0.030081 | -0.229314 | -0.225646 | -0.227129 | -0.227467 | -0.231161 | 0.338631 | 0.338631 | 0.338631 | 0.344032 | 0.346500 | 0.346500 | 0.346500 | 0.344032 | 0.345704 | 0.345704 | 0.345704 | 0.335959 | 0.321143 | 0.321143 | 0.321143 | 0.335959 | 0.239683 | 0.223933 | 0.223933 | 0.223933 | 0.239683 | 0.214493 | 0.161987 | 0.161987 | 0.161987 | 0.214493 |
| skill_moves | -0.018800 | -0.413659 | -0.363400 | 0.243675 | 0.255135 | 0.217407 | 0.138444 | 0.326654 | 1.000000 | 0.005619 | 0.025072 | 0.689423 | 0.748240 | 0.702009 | 0.756512 | 0.184926 | 0.492610 | -0.605735 | 0.641123 | 0.704324 | 0.414778 | 0.603214 | 0.693460 | 0.744145 | 0.688348 | 0.643231 | 0.502428 | 0.705934 | 0.559510 | 0.524851 | 0.578740 | 0.194143 | 0.465349 | 0.633273 | 0.006224 | 0.428807 | -0.116498 | 0.675824 | 0.240455 | 0.065951 | 0.705360 | 0.514639 | 0.646602 | 0.042260 | 0.097889 | 0.061889 | -0.603506 | -0.598528 | -0.598083 | -0.599330 | -0.602399 | 0.736327 | 0.736327 | 0.736327 | 0.757659 | 0.753949 | 0.753949 | 0.753949 | 0.757659 | 0.749065 | 0.749065 | 0.749065 | 0.741494 | 0.684234 | 0.684234 | 0.684234 | 0.741494 | 0.531699 | 0.478510 | 0.478510 | 0.478510 | 0.531699 | 0.477996 | 0.352840 | 0.352840 | 0.352840 | 0.477996 |
| team_jersey_number | -0.228214 | -0.043021 | -0.084685 | -0.179027 | -0.022922 | -0.065684 | -0.063367 | -0.043307 | 0.005619 | 1.000000 | 0.095653 | 0.000264 | -0.012507 | -0.053494 | -0.019520 | -0.106243 | -0.085053 | 0.009708 | -0.041065 | 0.011293 | -0.062259 | -0.064427 | 0.000833 | -0.001282 | -0.028309 | -0.042253 | -0.085934 | -0.042484 | 0.026925 | 0.015438 | 0.000150 | -0.138510 | 0.033491 | -0.032472 | -0.080415 | -0.088479 | -0.122524 | -0.014596 | -0.124978 | -0.130022 | -0.008546 | -0.074602 | -0.005210 | -0.103717 | -0.102330 | -0.090515 | -0.000528 | -0.006068 | -0.005026 | -0.006368 | -0.001365 | -0.031245 | -0.031245 | -0.031245 | -0.026383 | -0.028186 | -0.028186 | -0.028186 | -0.026383 | -0.033223 | -0.033223 | -0.033223 | -0.034475 | -0.058140 | -0.058140 | -0.058140 | -0.034475 | -0.078242 | -0.092148 | -0.092148 | -0.092148 | -0.078242 | -0.083102 | -0.098735 | -0.098735 | -0.098735 | -0.083102 |
| contract_valid_until | -0.151552 | -0.094536 | -0.078132 | -0.013182 | 0.085902 | 0.092588 | 0.002140 | -0.037653 | 0.025072 | 0.095653 | 1.000000 | 0.023659 | -0.008545 | -0.004539 | 0.010733 | -0.003745 | -0.012795 | -0.002950 | -0.006763 | 0.001818 | 0.001287 | 0.010506 | -0.009427 | 0.024451 | -0.012891 | -0.031438 | 0.000777 | -0.000693 | 0.045708 | 0.039327 | 0.006231 | -0.006916 | 0.025078 | -0.013704 | 0.000822 | 0.007775 | -0.034861 | -0.005254 | -0.040480 | -0.018834 | 0.003121 | -0.017567 | -0.004697 | 0.003763 | 0.001320 | 0.009542 | -0.010811 | -0.011434 | -0.012459 | -0.013338 | -0.010358 | -0.000028 | -0.000028 | -0.000028 | 0.004863 | 0.003806 | 0.003806 | 0.003806 | 0.004863 | 0.003808 | 0.003808 | 0.003808 | 0.005199 | 0.000444 | 0.000444 | 0.000444 | 0.005199 | 0.001424 | -0.003559 | -0.003559 | -0.003559 | 0.001424 | 0.001553 | -0.004776 | -0.004776 | -0.004776 | 0.001553 |
| pace | -0.164913 | -0.478165 | -0.451028 | 0.144062 | 0.213147 | 0.106889 | 0.017002 | 0.269767 | 0.689423 | 0.000264 | 0.023659 | 1.000000 | 0.800930 | 0.845809 | 0.913649 | 0.528046 | 0.787867 | -0.880618 | 0.774195 | 0.697979 | 0.662325 | 0.705381 | 0.683401 | 0.855954 | 0.722824 | 0.646110 | 0.592139 | 0.823366 | 0.796440 | 0.801431 | 0.670023 | 0.104890 | 0.555840 | 0.713885 | 0.159667 | 0.691661 | -0.042270 | 0.704026 | 0.471350 | 0.333314 | 0.777716 | 0.435799 | 0.662849 | 0.373159 | 0.414313 | 0.395538 | -0.873950 | -0.868274 | -0.865116 | -0.868614 | -0.873302 | 0.895389 | 0.895389 | 0.895389 | 0.916727 | 0.902720 | 0.902720 | 0.902720 | 0.916727 | 0.897215 | 0.897215 | 0.897215 | 0.918541 | 0.868593 | 0.868593 | 0.868593 | 0.918541 | 0.832935 | 0.764888 | 0.764888 | 0.764888 | 0.832935 | 0.800295 | 0.684496 | 0.684496 | 0.684496 | 0.800295 |
| shooting | 0.048660 | -0.378375 | -0.320896 | 0.314795 | 0.282428 | 0.212288 | 0.160887 | 0.361478 | 0.748240 | -0.012507 | -0.008545 | 0.800930 | 1.000000 | 0.874241 | 0.912135 | 0.324036 | 0.705047 | -0.763411 | 0.770076 | 0.944084 | 0.647219 | 0.765730 | 0.900608 | 0.887695 | 0.833123 | 0.795811 | 0.640998 | 0.874899 | 0.571517 | 0.560447 | 0.598910 | 0.283622 | 0.456179 | 0.893741 | 0.082872 | 0.590598 | 0.026450 | 0.927343 | 0.419787 | 0.168501 | 0.910771 | 0.632900 | 0.862874 | 0.141720 | 0.201477 | 0.154064 | -0.758078 | -0.752263 | -0.749882 | -0.753777 | -0.757140 | 0.958163 | 0.958163 | 0.958163 | 0.938259 | 0.951668 | 0.951668 | 0.951668 | 0.938259 | 0.936173 | 0.936173 | 0.936173 | 0.918355 | 0.876037 | 0.876037 | 0.876037 | 0.918355 | 0.698758 | 0.656536 | 0.656536 | 0.656536 | 0.698758 | 0.643723 | 0.527674 | 0.527674 | 0.527674 | 0.643723 |
| passing | 0.062909 | -0.425457 | -0.379390 | 0.356585 | 0.314101 | 0.214805 | 0.163882 | 0.332389 | 0.702009 | -0.053494 | -0.004539 | 0.845809 | 0.874241 | 1.000000 | 0.954585 | 0.628087 | 0.815567 | -0.849273 | 0.901292 | 0.745060 | 0.694386 | 0.916255 | 0.770690 | 0.900838 | 0.862822 | 0.830440 | 0.848701 | 0.926499 | 0.559334 | 0.544179 | 0.594130 | 0.309449 | 0.501967 | 0.820885 | 0.103986 | 0.696999 | 0.036644 | 0.831794 | 0.590796 | 0.485877 | 0.835317 | 0.657614 | 0.752145 | 0.471315 | 0.526870 | 0.488785 | -0.842429 | -0.836956 | -0.834337 | -0.837036 | -0.841635 | 0.940656 | 0.940656 | 0.940656 | 0.965312 | 0.960350 | 0.960350 | 0.960350 | 0.965312 | 0.975713 | 0.975713 | 0.975713 | 0.975841 | 0.987252 | 0.987252 | 0.987252 | 0.975841 | 0.908033 | 0.883237 | 0.883237 | 0.883237 | 0.908033 | 0.869601 | 0.770362 | 0.770362 | 0.770362 | 0.869601 |
| dribbling | -0.026930 | -0.481723 | -0.434535 | 0.300196 | 0.308350 | 0.194875 | 0.123161 | 0.337112 | 0.756512 | -0.019520 | 0.010733 | 0.913649 | 0.912135 | 0.954585 | 1.000000 | 0.524436 | 0.792629 | -0.867221 | 0.864360 | 0.808459 | 0.683927 | 0.845044 | 0.806689 | 0.965906 | 0.846774 | 0.784268 | 0.735686 | 0.947548 | 0.663758 | 0.643874 | 0.680627 | 0.265283 | 0.564826 | 0.821653 | 0.116811 | 0.684340 | -0.024431 | 0.841241 | 0.514197 | 0.356018 | 0.880288 | 0.606899 | 0.776331 | 0.355289 | 0.408721 | 0.375334 | -0.860783 | -0.855039 | -0.852224 | -0.855846 | -0.859972 | 0.969502 | 0.969502 | 0.969502 | 0.992113 | 0.986707 | 0.986707 | 0.986707 | 0.992113 | 0.988354 | 0.988354 | 0.988354 | 0.990349 | 0.962021 | 0.962021 | 0.962021 | 0.990349 | 0.860507 | 0.812096 | 0.812096 | 0.812096 | 0.860507 | 0.814175 | 0.695236 | 0.695236 | 0.695236 | 0.814175 |
| defending | 0.100032 | -0.094135 | -0.094449 | 0.250692 | 0.177525 | 0.086131 | 0.088488 | 0.088885 | 0.184926 | -0.106243 | -0.003745 | 0.528046 | 0.324036 | 0.628087 | 0.524436 | 1.000000 | 0.793093 | -0.675525 | 0.522255 | 0.134424 | 0.693960 | 0.581427 | 0.218502 | 0.415491 | 0.383564 | 0.392132 | 0.604133 | 0.513999 | 0.176681 | 0.201683 | 0.139224 | 0.182920 | 0.145764 | 0.419787 | 0.217069 | 0.598906 | 0.306040 | 0.311583 | 0.759105 | 0.900588 | 0.297375 | 0.182386 | 0.262622 | 0.950341 | 0.962533 | 0.946758 | -0.668346 | -0.666287 | -0.662787 | -0.662892 | -0.669583 | 0.526746 | 0.526746 | 0.526746 | 0.523097 | 0.516990 | 0.516990 | 0.516990 | 0.523097 | 0.543815 | 0.543815 | 0.543815 | 0.571736 | 0.683010 | 0.683010 | 0.683010 | 0.571736 | 0.875960 | 0.911355 | 0.911355 | 0.911355 | 0.875960 | 0.914383 | 0.966683 | 0.966683 | 0.966683 | 0.914383 |
| physic | 0.069155 | -0.137255 | -0.100458 | 0.251938 | 0.182002 | 0.107833 | 0.080794 | 0.215682 | 0.492610 | -0.085053 | -0.012795 | 0.787867 | 0.705047 | 0.815567 | 0.792629 | 0.793093 | 1.000000 | -0.894652 | 0.666157 | 0.546773 | 0.874765 | 0.712310 | 0.574340 | 0.692573 | 0.607215 | 0.587034 | 0.629375 | 0.757520 | 0.387746 | 0.422150 | 0.330229 | 0.193011 | 0.224638 | 0.710323 | 0.237934 | 0.757023 | 0.415120 | 0.616825 | 0.769158 | 0.613573 | 0.645750 | 0.348580 | 0.595183 | 0.644740 | 0.685592 | 0.643789 | -0.886312 | -0.881632 | -0.879163 | -0.879946 | -0.887810 | 0.848474 | 0.848474 | 0.848474 | 0.808134 | 0.816910 | 0.816910 | 0.816910 | 0.808134 | 0.817815 | 0.817815 | 0.817815 | 0.832759 | 0.874412 | 0.874412 | 0.874412 | 0.832759 | 0.915032 | 0.920885 | 0.920885 | 0.920885 | 0.915032 | 0.915544 | 0.914284 | 0.914284 | 0.914284 | 0.915544 |
| gk_speed | 0.086077 | 0.337328 | 0.323379 | -0.050719 | -0.080463 | -0.015566 | 0.005066 | -0.233476 | -0.605735 | 0.009708 | -0.002950 | -0.880618 | -0.763411 | -0.849273 | -0.867221 | -0.675525 | -0.894652 | 1.000000 | -0.708400 | -0.627433 | -0.790570 | -0.699086 | -0.629532 | -0.774641 | -0.661731 | -0.629654 | -0.597333 | -0.790668 | -0.463939 | -0.465746 | -0.435507 | -0.028167 | -0.384146 | -0.699581 | -0.095970 | -0.645944 | -0.088828 | -0.654188 | -0.568473 | -0.453756 | -0.705665 | -0.368787 | -0.644618 | -0.514756 | -0.556996 | -0.526258 | 0.959659 | 0.951587 | 0.950879 | 0.950258 | 0.958423 | -0.882866 | -0.882866 | -0.882866 | -0.870797 | -0.870610 | -0.870610 | -0.870610 | -0.870797 | -0.871464 | -0.871464 | -0.871464 | -0.883713 | -0.887769 | -0.887769 | -0.887769 | -0.883713 | -0.881761 | -0.857812 | -0.857812 | -0.857812 | -0.881761 | -0.866853 | -0.813820 | -0.813820 | -0.813820 | -0.866853 |
| attacking_crossing | 0.062796 | -0.458348 | -0.405305 | 0.337289 | 0.289771 | 0.195654 | 0.142636 | 0.301782 | 0.641123 | -0.041065 | -0.006763 | 0.774195 | 0.770076 | 0.901292 | 0.864360 | 0.522255 | 0.666157 | -0.708400 | 1.000000 | 0.675164 | 0.545065 | 0.800872 | 0.702745 | 0.850750 | 0.831344 | 0.773463 | 0.751791 | 0.834426 | 0.589846 | 0.573909 | 0.610416 | 0.296189 | 0.520314 | 0.721327 | 0.087195 | 0.633683 | -0.043699 | 0.759907 | 0.486414 | 0.413056 | 0.781636 | 0.590592 | 0.663582 | 0.405365 | 0.448174 | 0.427599 | -0.706749 | -0.702701 | -0.699935 | -0.703763 | -0.705759 | 0.822018 | 0.822018 | 0.822018 | 0.875131 | 0.851572 | 0.851572 | 0.851572 | 0.875131 | 0.862944 | 0.862944 | 0.862944 | 0.881982 | 0.859988 | 0.859988 | 0.859988 | 0.881982 | 0.808197 | 0.751275 | 0.751275 | 0.751275 | 0.808197 | 0.764308 | 0.640622 | 0.640622 | 0.640622 | 0.764308 |
| attacking_finishing | 0.005349 | -0.358111 | -0.312001 | 0.258967 | 0.255918 | 0.194837 | 0.130259 | 0.342898 | 0.704324 | 0.011293 | 0.001818 | 0.697979 | 0.944084 | 0.745060 | 0.808459 | 0.134424 | 0.546773 | -0.627433 | 0.675164 | 1.000000 | 0.526821 | 0.653716 | 0.863921 | 0.819004 | 0.754751 | 0.710470 | 0.517743 | 0.779758 | 0.544159 | 0.528919 | 0.570481 | 0.248193 | 0.429667 | 0.783201 | 0.043537 | 0.477330 | -0.034551 | 0.854079 | 0.267060 | 0.000603 | 0.870155 | 0.595582 | 0.813355 | -0.029025 | 0.026011 | -0.020106 | -0.627670 | -0.622927 | -0.619716 | -0.626061 | -0.626086 | 0.859337 | 0.859337 | 0.859337 | 0.838406 | 0.852345 | 0.852345 | 0.852345 | 0.838406 | 0.830024 | 0.830024 | 0.830024 | 0.807571 | 0.739998 | 0.739998 | 0.739998 | 0.807571 | 0.528117 | 0.477998 | 0.477998 | 0.477998 | 0.528117 | 0.466539 | 0.341281 | 0.341281 | 0.341281 | 0.466539 |
| attacking_heading_accuracy | 0.080368 | -0.028599 | -0.033478 | 0.285335 | 0.225003 | 0.135334 | 0.124098 | 0.192889 | 0.414778 | -0.062259 | 0.001287 | 0.662325 | 0.647219 | 0.694386 | 0.683927 | 0.693960 | 0.874765 | -0.790570 | 0.545065 | 0.526821 | 1.000000 | 0.623552 | 0.544204 | 0.598998 | 0.514431 | 0.491310 | 0.523356 | 0.675608 | 0.276705 | 0.320428 | 0.221345 | 0.218165 | 0.116292 | 0.657084 | 0.254868 | 0.592849 | 0.382820 | 0.550307 | 0.659538 | 0.512355 | 0.579896 | 0.269583 | 0.571513 | 0.553592 | 0.582708 | 0.543606 | -0.787212 | -0.784757 | -0.779547 | -0.782220 | -0.787717 | 0.775686 | 0.775686 | 0.775686 | 0.702986 | 0.723562 | 0.723562 | 0.723562 | 0.702986 | 0.714178 | 0.714178 | 0.714178 | 0.719612 | 0.758542 | 0.758542 | 0.758542 | 0.719612 | 0.781133 | 0.794573 | 0.794573 | 0.794573 | 0.781133 | 0.789995 | 0.811381 | 0.811381 | 0.811381 | 0.789995 |
| attacking_short_passing | 0.086728 | -0.341943 | -0.302296 | 0.462311 | 0.419149 | 0.272554 | 0.198796 | 0.306543 | 0.603214 | -0.064427 | 0.010506 | 0.705381 | 0.765730 | 0.916255 | 0.845044 | 0.581427 | 0.712310 | -0.699086 | 0.800872 | 0.653716 | 0.623552 | 1.000000 | 0.681397 | 0.811591 | 0.767307 | 0.737667 | 0.884123 | 0.873523 | 0.473710 | 0.463438 | 0.521574 | 0.409666 | 0.429366 | 0.753900 | 0.116423 | 0.634016 | 0.082916 | 0.752893 | 0.571531 | 0.487002 | 0.735982 | 0.640776 | 0.663619 | 0.450958 | 0.505908 | 0.462825 | -0.694399 | -0.689938 | -0.686612 | -0.689461 | -0.693534 | 0.829176 | 0.829176 | 0.829176 | 0.853626 | 0.852871 | 0.852871 | 0.852871 | 0.853626 | 0.875312 | 0.875312 | 0.875312 | 0.867623 | 0.900025 | 0.900025 | 0.900025 | 0.867623 | 0.817747 | 0.813068 | 0.813068 | 0.813068 | 0.817747 | 0.783080 | 0.700933 | 0.700933 | 0.700933 | 0.783080 |
| attacking_volleys | 0.061369 | -0.344828 | -0.291449 | 0.313773 | 0.279155 | 0.229246 | 0.192290 | 0.346696 | 0.693460 | 0.000833 | -0.009427 | 0.683401 | 0.900608 | 0.770690 | 0.806689 | 0.218502 | 0.574340 | -0.629532 | 0.702745 | 0.863921 | 0.544204 | 0.681397 | 1.000000 | 0.805800 | 0.793098 | 0.745444 | 0.566144 | 0.782048 | 0.513073 | 0.495831 | 0.559215 | 0.293150 | 0.427023 | 0.797948 | 0.066621 | 0.499465 | -0.007675 | 0.846999 | 0.339563 | 0.098818 | 0.836505 | 0.602356 | 0.795701 | 0.062621 | 0.118518 | 0.076556 | -0.629848 | -0.625313 | -0.621765 | -0.627386 | -0.628249 | 0.843690 | 0.843690 | 0.843690 | 0.829032 | 0.840686 | 0.840686 | 0.840686 | 0.829032 | 0.826355 | 0.826355 | 0.826355 | 0.807050 | 0.760627 | 0.760627 | 0.760627 | 0.807050 | 0.577657 | 0.535637 | 0.535637 | 0.535637 | 0.577657 | 0.521969 | 0.407616 | 0.407616 | 0.407616 | 0.521969 |
| skill_dribbling | -0.051007 | -0.475107 | -0.431251 | 0.301068 | 0.328817 | 0.204294 | 0.121119 | 0.333929 | 0.744145 | -0.001282 | 0.024451 | 0.855954 | 0.887695 | 0.900838 | 0.965906 | 0.415491 | 0.692573 | -0.774641 | 0.850750 | 0.819004 | 0.598998 | 0.811591 | 0.805800 | 1.000000 | 0.838454 | 0.768556 | 0.701822 | 0.918433 | 0.668251 | 0.645656 | 0.673653 | 0.270693 | 0.555582 | 0.794862 | 0.078759 | 0.631916 | -0.063770 | 0.838788 | 0.431736 | 0.270540 | 0.878515 | 0.623687 | 0.760631 | 0.262768 | 0.315544 | 0.284408 | -0.772031 | -0.766865 | -0.763112 | -0.769358 | -0.771108 | 0.919107 | 0.919107 | 0.919107 | 0.950743 | 0.943775 | 0.943775 | 0.943775 | 0.950743 | 0.942851 | 0.942851 | 0.942851 | 0.943085 | 0.898746 | 0.898746 | 0.898746 | 0.943085 | 0.772177 | 0.715617 | 0.715617 | 0.715617 | 0.772177 | 0.718024 | 0.586209 | 0.586209 | 0.586209 | 0.718024 |
| skill_curve | 0.070079 | -0.421572 | -0.368087 | 0.354916 | 0.316465 | 0.226008 | 0.173613 | 0.335910 | 0.688348 | -0.028309 | -0.012891 | 0.722824 | 0.833123 | 0.862822 | 0.846774 | 0.383564 | 0.607215 | -0.661731 | 0.831344 | 0.754751 | 0.514431 | 0.767307 | 0.793098 | 0.838454 | 1.000000 | 0.850194 | 0.699538 | 0.825819 | 0.550957 | 0.529230 | 0.606768 | 0.316768 | 0.495136 | 0.770820 | 0.057323 | 0.564913 | -0.052682 | 0.826953 | 0.411241 | 0.282946 | 0.807997 | 0.635218 | 0.746328 | 0.246658 | 0.301985 | 0.268331 | -0.661427 | -0.656669 | -0.654681 | -0.660964 | -0.660563 | 0.829595 | 0.829595 | 0.829595 | 0.857413 | 0.852536 | 0.852536 | 0.852536 | 0.857413 | 0.856633 | 0.856633 | 0.856633 | 0.850916 | 0.827431 | 0.827431 | 0.827431 | 0.850916 | 0.702371 | 0.659306 | 0.659306 | 0.659306 | 0.702371 | 0.651881 | 0.530464 | 0.530464 | 0.530464 | 0.651881 |
| skill_fk_accuracy | 0.122131 | -0.384103 | -0.326886 | 0.329594 | 0.261308 | 0.204509 | 0.165791 | 0.321467 | 0.643231 | -0.042253 | -0.031438 | 0.646110 | 0.795811 | 0.830440 | 0.784268 | 0.392132 | 0.587034 | -0.629654 | 0.773463 | 0.710470 | 0.491310 | 0.737667 | 0.745444 | 0.768556 | 0.850194 | 1.000000 | 0.693794 | 0.770411 | 0.455804 | 0.427267 | 0.531066 | 0.299935 | 0.445329 | 0.749776 | 0.037720 | 0.526272 | -0.028095 | 0.804177 | 0.418455 | 0.305571 | 0.744764 | 0.618790 | 0.738011 | 0.262319 | 0.318619 | 0.280936 | -0.628741 | -0.623520 | -0.620866 | -0.627084 | -0.626592 | 0.780259 | 0.780259 | 0.780259 | 0.800120 | 0.799696 | 0.799696 | 0.799696 | 0.800120 | 0.807038 | 0.807038 | 0.807038 | 0.795987 | 0.791672 | 0.791672 | 0.791672 | 0.795987 | 0.672190 | 0.645073 | 0.645073 | 0.645073 | 0.672190 | 0.627274 | 0.524233 | 0.524233 | 0.524233 | 0.627274 |
| skill_long_passing | 0.139071 | -0.319234 | -0.279506 | 0.449014 | 0.374957 | 0.253297 | 0.197237 | 0.273411 | 0.502428 | -0.085934 | 0.000777 | 0.592139 | 0.640998 | 0.848701 | 0.735686 | 0.604133 | 0.629375 | -0.597333 | 0.751791 | 0.517743 | 0.523356 | 0.884123 | 0.566144 | 0.701822 | 0.699538 | 0.693794 | 1.000000 | 0.766071 | 0.385430 | 0.372229 | 0.456365 | 0.388877 | 0.387601 | 0.659658 | 0.109985 | 0.583382 | 0.067763 | 0.667167 | 0.556592 | 0.547917 | 0.607318 | 0.611578 | 0.550824 | 0.504891 | 0.550589 | 0.517332 | -0.593282 | -0.588155 | -0.585491 | -0.588310 | -0.591246 | 0.704655 | 0.704655 | 0.704655 | 0.741146 | 0.735400 | 0.735400 | 0.735400 | 0.741146 | 0.768470 | 0.768470 | 0.768470 | 0.765193 | 0.823421 | 0.823421 | 0.823421 | 0.765193 | 0.769808 | 0.781576 | 0.781576 | 0.781576 | 0.769808 | 0.744346 | 0.677501 | 0.677501 | 0.677501 | 0.744346 |
| skill_ball_control | 0.029144 | -0.394236 | -0.353959 | 0.406070 | 0.391883 | 0.245157 | 0.164953 | 0.338561 | 0.705934 | -0.042484 | -0.000693 | 0.823366 | 0.874899 | 0.926499 | 0.947548 | 0.513999 | 0.757520 | -0.790668 | 0.834426 | 0.779758 | 0.675608 | 0.873523 | 0.782048 | 0.918433 | 0.825819 | 0.770411 | 0.766071 | 1.000000 | 0.585556 | 0.573960 | 0.609808 | 0.355175 | 0.486774 | 0.822409 | 0.118261 | 0.655850 | 0.042078 | 0.827870 | 0.530494 | 0.376468 | 0.843712 | 0.628627 | 0.758144 | 0.357393 | 0.410996 | 0.371727 | -0.784465 | -0.780271 | -0.776465 | -0.779936 | -0.784129 | 0.926808 | 0.926808 | 0.926808 | 0.943087 | 0.943181 | 0.943181 | 0.943181 | 0.943087 | 0.947908 | 0.947908 | 0.947908 | 0.943578 | 0.933271 | 0.933271 | 0.933271 | 0.943578 | 0.824830 | 0.792925 | 0.792925 | 0.792925 | 0.824830 | 0.780885 | 0.675669 | 0.675669 | 0.675669 | 0.780885 |
| movement_acceleration | -0.224824 | -0.515279 | -0.484357 | 0.157273 | 0.265961 | 0.152016 | 0.016482 | 0.216583 | 0.559510 | 0.026925 | 0.045708 | 0.796440 | 0.571517 | 0.559334 | 0.663758 | 0.176681 | 0.387746 | -0.463939 | 0.589846 | 0.544159 | 0.276705 | 0.473710 | 0.513073 | 0.668251 | 0.550957 | 0.455804 | 0.385430 | 0.585556 | 1.000000 | 0.865255 | 0.734950 | 0.130906 | 0.603083 | 0.478476 | 0.168976 | 0.508321 | -0.222655 | 0.525611 | 0.183487 | 0.075532 | 0.601232 | 0.366858 | 0.459133 | 0.080902 | 0.106630 | 0.110050 | -0.493847 | -0.491974 | -0.488022 | -0.493843 | -0.493340 | 0.604166 | 0.604166 | 0.604166 | 0.661794 | 0.635606 | 0.635606 | 0.635606 | 0.661794 | 0.626725 | 0.626725 | 0.626725 | 0.649924 | 0.554812 | 0.554812 | 0.554812 | 0.649924 | 0.493498 | 0.398022 | 0.398022 | 0.398022 | 0.493498 | 0.450753 | 0.298822 | 0.298822 | 0.298822 | 0.450753 |
| movement_sprint_speed | -0.218302 | -0.435221 | -0.413137 | 0.176556 | 0.278954 | 0.153989 | 0.014446 | 0.205218 | 0.524851 | 0.015438 | 0.039327 | 0.801431 | 0.560447 | 0.544179 | 0.643874 | 0.201683 | 0.422150 | -0.465746 | 0.573909 | 0.528919 | 0.320428 | 0.463438 | 0.495831 | 0.645656 | 0.529230 | 0.427267 | 0.372229 | 0.573960 | 0.865255 | 1.000000 | 0.681146 | 0.135018 | 0.528762 | 0.486232 | 0.186220 | 0.519681 | -0.139247 | 0.505146 | 0.216984 | 0.094755 | 0.585666 | 0.333093 | 0.448614 | 0.105869 | 0.131338 | 0.132122 | -0.495886 | -0.495793 | -0.490757 | -0.497843 | -0.496280 | 0.603702 | 0.603702 | 0.603702 | 0.647820 | 0.624833 | 0.624833 | 0.624833 | 0.647820 | 0.612193 | 0.612193 | 0.612193 | 0.638277 | 0.547184 | 0.547184 | 0.547184 | 0.638277 | 0.501434 | 0.408964 | 0.408964 | 0.408964 | 0.501434 | 0.463781 | 0.325201 | 0.325201 | 0.325201 | 0.463781 |
| movement_agility | -0.073392 | -0.599136 | -0.546209 | 0.211860 | 0.246533 | 0.159247 | 0.056832 | 0.257929 | 0.578740 | 0.000150 | 0.006231 | 0.670023 | 0.598910 | 0.594130 | 0.680627 | 0.139224 | 0.330229 | -0.435507 | 0.610416 | 0.570481 | 0.221345 | 0.521574 | 0.559215 | 0.673653 | 0.606768 | 0.531066 | 0.456365 | 0.609808 | 0.734950 | 0.681146 | 1.000000 | 0.208564 | 0.682670 | 0.491919 | 0.182293 | 0.459095 | -0.295513 | 0.576066 | 0.179222 | 0.065143 | 0.618571 | 0.485716 | 0.494055 | 0.038587 | 0.067829 | 0.067121 | -0.442482 | -0.440033 | -0.439042 | -0.442835 | -0.441984 | 0.594003 | 0.594003 | 0.594003 | 0.661655 | 0.636283 | 0.636283 | 0.636283 | 0.661655 | 0.640666 | 0.640666 | 0.640666 | 0.643769 | 0.570597 | 0.570597 | 0.570597 | 0.643769 | 0.463525 | 0.385108 | 0.385108 | 0.385108 | 0.463525 | 0.411821 | 0.259553 | 0.259553 | 0.259553 | 0.411821 |
| movement_reactions | 0.395858 | 0.003806 | 0.071590 | 0.792814 | 0.590323 | 0.471799 | 0.384748 | 0.175951 | 0.194143 | -0.138510 | -0.006916 | 0.104890 | 0.283622 | 0.309449 | 0.265283 | 0.182920 | 0.193011 | -0.028167 | 0.296189 | 0.248193 | 0.218165 | 0.409666 | 0.293150 | 0.270693 | 0.316768 | 0.299935 | 0.388877 | 0.355175 | 0.130906 | 0.135018 | 0.208564 | 1.000000 | 0.074565 | 0.320461 | 0.203560 | 0.272519 | 0.214872 | 0.317900 | 0.336892 | 0.249985 | 0.297775 | 0.429489 | 0.257361 | 0.139241 | 0.160398 | 0.133325 | -0.000527 | 0.002699 | -0.000727 | 0.005807 | -0.001361 | 0.284001 | 0.284001 | 0.284001 | 0.281018 | 0.292871 | 0.292871 | 0.292871 | 0.281018 | 0.295602 | 0.295602 | 0.295602 | 0.282749 | 0.313722 | 0.313722 | 0.313722 | 0.282749 | 0.260861 | 0.273106 | 0.273106 | 0.273106 | 0.260861 | 0.245538 | 0.222267 | 0.222267 | 0.222267 | 0.245538 |
| movement_balance | -0.123581 | -0.787934 | -0.697291 | 0.037695 | 0.109251 | 0.065007 | 0.004914 | 0.204510 | 0.465349 | 0.033491 | 0.025078 | 0.555840 | 0.456179 | 0.501967 | 0.564826 | 0.145764 | 0.224638 | -0.384146 | 0.520314 | 0.429667 | 0.116292 | 0.429366 | 0.427023 | 0.555582 | 0.495136 | 0.445329 | 0.387601 | 0.486774 | 0.603083 | 0.528762 | 0.682670 | 0.074565 | 1.000000 | 0.363177 | 0.143657 | 0.353096 | -0.446657 | 0.451306 | 0.115319 | 0.083790 | 0.487201 | 0.376408 | 0.386284 | 0.079182 | 0.097653 | 0.111921 | -0.401418 | -0.399526 | -0.395503 | -0.401498 | -0.399564 | 0.459218 | 0.459218 | 0.459218 | 0.537243 | 0.510825 | 0.510825 | 0.510825 | 0.537243 | 0.521549 | 0.521549 | 0.521549 | 0.528261 | 0.473680 | 0.473680 | 0.473680 | 0.528261 | 0.400874 | 0.332768 | 0.332768 | 0.332768 | 0.400874 | 0.358966 | 0.222051 | 0.222051 | 0.222051 | 0.358966 |
| power_shot_power | 0.088910 | -0.281572 | -0.220455 | 0.376679 | 0.322904 | 0.217239 | 0.172561 | 0.314252 | 0.633273 | -0.032472 | -0.013704 | 0.713885 | 0.893741 | 0.820885 | 0.821653 | 0.419787 | 0.710323 | -0.699581 | 0.721327 | 0.783201 | 0.657084 | 0.753900 | 0.797948 | 0.794862 | 0.770820 | 0.749776 | 0.659658 | 0.822409 | 0.478476 | 0.486232 | 0.491919 | 0.320461 | 0.363177 | 1.000000 | 0.115005 | 0.589868 | 0.135769 | 0.862322 | 0.508822 | 0.292827 | 0.792400 | 0.567777 | 0.772706 | 0.266954 | 0.320677 | 0.276584 | -0.697839 | -0.693702 | -0.690823 | -0.694882 | -0.696986 | 0.876177 | 0.876177 | 0.876177 | 0.844244 | 0.862492 | 0.862492 | 0.862492 | 0.844244 | 0.850102 | 0.850102 | 0.850102 | 0.836326 | 0.828000 | 0.828000 | 0.828000 | 0.836326 | 0.705215 | 0.684018 | 0.684018 | 0.684018 | 0.705215 | 0.665902 | 0.585250 | 0.585250 | 0.585250 | 0.665902 |
| power_jumping | 0.121634 | -0.073728 | -0.024147 | 0.237238 | 0.160963 | 0.110937 | 0.093846 | 0.049819 | 0.006224 | -0.080415 | 0.000822 | 0.159667 | 0.082872 | 0.103986 | 0.116811 | 0.217069 | 0.237934 | -0.095970 | 0.087195 | 0.043537 | 0.254868 | 0.116423 | 0.066621 | 0.078759 | 0.057323 | 0.037720 | 0.109985 | 0.118261 | 0.168976 | 0.186220 | 0.182293 | 0.203560 | 0.143657 | 0.115005 | 1.000000 | 0.242498 | 0.172379 | 0.069583 | 0.264087 | 0.210933 | 0.080256 | 0.034678 | 0.070303 | 0.189044 | 0.185032 | 0.192103 | -0.101529 | -0.101283 | -0.099919 | -0.099698 | -0.102472 | 0.139095 | 0.139095 | 0.139095 | 0.118867 | 0.120011 | 0.120011 | 0.120011 | 0.118867 | 0.115986 | 0.115986 | 0.115986 | 0.126938 | 0.140109 | 0.140109 | 0.140109 | 0.126938 | 0.196337 | 0.196919 | 0.196919 | 0.196919 | 0.196337 | 0.209495 | 0.236918 | 0.236918 | 0.236918 | 0.209495 |
| power_stamina | 0.023215 | -0.259871 | -0.231913 | 0.303921 | 0.246595 | 0.164818 | 0.068915 | 0.204976 | 0.428807 | -0.088479 | 0.007775 | 0.691661 | 0.590598 | 0.696999 | 0.684340 | 0.598906 | 0.757023 | -0.645944 | 0.633683 | 0.477330 | 0.592849 | 0.634016 | 0.499465 | 0.631916 | 0.564913 | 0.526272 | 0.583382 | 0.655850 | 0.508321 | 0.519681 | 0.459095 | 0.272519 | 0.353096 | 0.589868 | 0.242498 | 1.000000 | 0.191492 | 0.564325 | 0.583810 | 0.501858 | 0.591054 | 0.397458 | 0.484088 | 0.496106 | 0.529711 | 0.503195 | -0.642126 | -0.639228 | -0.636662 | -0.638767 | -0.643495 | 0.688676 | 0.688676 | 0.688676 | 0.695161 | 0.689726 | 0.689726 | 0.689726 | 0.695161 | 0.695555 | 0.695555 | 0.695555 | 0.720161 | 0.737206 | 0.737206 | 0.737206 | 0.720161 | 0.764014 | 0.736461 | 0.736461 | 0.736461 | 0.764014 | 0.748729 | 0.685520 | 0.685520 | 0.685520 | 0.748729 |
| power_strength | 0.259827 | 0.531675 | 0.595008 | 0.292941 | 0.128617 | 0.101879 | 0.112206 | -0.020808 | -0.116498 | -0.122524 | -0.034861 | -0.042270 | 0.026450 | 0.036644 | -0.024431 | 0.306040 | 0.415120 | -0.088828 | -0.043699 | -0.034551 | 0.382820 | 0.082916 | -0.007675 | -0.063770 | -0.052682 | -0.028095 | 0.067763 | 0.042078 | -0.222655 | -0.139247 | -0.295513 | 0.214872 | -0.446657 | 0.135769 | 0.172379 | 0.191492 | 1.000000 | 0.003175 | 0.391389 | 0.301374 | -0.020422 | -0.053100 | 0.021208 | 0.286243 | 0.295564 | 0.255583 | -0.084551 | -0.081549 | -0.084190 | -0.078650 | -0.087483 | 0.098058 | 0.098058 | 0.098058 | 0.004797 | 0.032616 | 0.032616 | 0.032616 | 0.004797 | 0.023525 | 0.023525 | 0.023525 | 0.024305 | 0.093435 | 0.093435 | 0.093435 | 0.024305 | 0.168889 | 0.233944 | 0.233944 | 0.233944 | 0.168889 | 0.201371 | 0.323689 | 0.323689 | 0.323689 | 0.201371 |
| power_long_shots | 0.076456 | -0.379441 | -0.316240 | 0.341751 | 0.296538 | 0.216411 | 0.161859 | 0.347918 | 0.675824 | -0.014596 | -0.005254 | 0.704026 | 0.927343 | 0.831794 | 0.841241 | 0.311583 | 0.616825 | -0.654188 | 0.759907 | 0.854079 | 0.550307 | 0.752893 | 0.846999 | 0.838788 | 0.826953 | 0.804177 | 0.667167 | 0.827870 | 0.525611 | 0.505146 | 0.576066 | 0.317900 | 0.451306 | 0.862322 | 0.069583 | 0.564325 | 0.003175 | 1.000000 | 0.400693 | 0.203746 | 0.845436 | 0.647124 | 0.789812 | 0.159072 | 0.216314 | 0.171608 | -0.654598 | -0.650759 | -0.646422 | -0.651769 | -0.653411 | 0.867660 | 0.867660 | 0.867660 | 0.864848 | 0.874767 | 0.874767 | 0.874767 | 0.864848 | 0.868562 | 0.868562 | 0.868562 | 0.846400 | 0.822542 | 0.822542 | 0.822542 | 0.846400 | 0.652589 | 0.617293 | 0.617293 | 0.617293 | 0.652589 | 0.598755 | 0.484314 | 0.484314 | 0.484314 | 0.598755 |
| mentality_aggression | 0.237996 | -0.045248 | -0.002919 | 0.391474 | 0.241544 | 0.155696 | 0.145597 | 0.138597 | 0.240455 | -0.124978 | -0.040480 | 0.471350 | 0.419787 | 0.590796 | 0.514197 | 0.759105 | 0.769158 | -0.568473 | 0.486414 | 0.267060 | 0.659538 | 0.571531 | 0.339563 | 0.431736 | 0.411241 | 0.418455 | 0.556592 | 0.530494 | 0.183487 | 0.216984 | 0.179222 | 0.336892 | 0.115319 | 0.508822 | 0.264087 | 0.583810 | 0.391389 | 0.400693 | 1.000000 | 0.695470 | 0.390661 | 0.276910 | 0.344994 | 0.680253 | 0.709337 | 0.678957 | -0.558962 | -0.555333 | -0.553184 | -0.551825 | -0.560739 | 0.554021 | 0.554021 | 0.554021 | 0.526201 | 0.533836 | 0.533836 | 0.533836 | 0.526201 | 0.545512 | 0.545512 | 0.545512 | 0.558113 | 0.639390 | 0.639390 | 0.639390 | 0.558113 | 0.734497 | 0.775185 | 0.775185 | 0.775185 | 0.734497 | 0.752965 | 0.800709 | 0.800709 | 0.800709 | 0.752965 |
| mentality_interceptions | 0.167387 | -0.038329 | -0.035367 | 0.306587 | 0.199210 | 0.114374 | 0.108258 | 0.056026 | 0.065951 | -0.130022 | -0.018834 | 0.333314 | 0.168501 | 0.485877 | 0.356018 | 0.900588 | 0.613573 | -0.453756 | 0.413056 | 0.000603 | 0.512355 | 0.487002 | 0.098818 | 0.270540 | 0.282946 | 0.305571 | 0.547917 | 0.376468 | 0.075532 | 0.094755 | 0.065143 | 0.249985 | 0.083790 | 0.292827 | 0.210933 | 0.501858 | 0.301374 | 0.203746 | 0.695470 | 1.000000 | 0.168677 | 0.160827 | 0.127833 | 0.876480 | 0.890864 | 0.873848 | -0.453764 | -0.453750 | -0.448732 | -0.449937 | -0.455025 | 0.346869 | 0.346869 | 0.346869 | 0.354886 | 0.347690 | 0.347690 | 0.347690 | 0.354886 | 0.380442 | 0.380442 | 0.380442 | 0.404170 | 0.530829 | 0.530829 | 0.530829 | 0.404170 | 0.728475 | 0.776615 | 0.776615 | 0.776615 | 0.728475 | 0.767959 | 0.829185 | 0.829185 | 0.829185 | 0.767959 |
| mentality_positioning | 0.014522 | -0.414091 | -0.360297 | 0.286826 | 0.272169 | 0.200393 | 0.135066 | 0.335953 | 0.705360 | -0.008546 | 0.003121 | 0.777716 | 0.910771 | 0.835317 | 0.880288 | 0.297375 | 0.645750 | -0.705665 | 0.781636 | 0.870155 | 0.579896 | 0.735982 | 0.836505 | 0.878515 | 0.807997 | 0.744764 | 0.607318 | 0.843712 | 0.601232 | 0.585666 | 0.618571 | 0.297775 | 0.487201 | 0.792400 | 0.080256 | 0.591054 | -0.020422 | 0.845436 | 0.390661 | 0.168677 | 1.000000 | 0.631142 | 0.786006 | 0.134477 | 0.192662 | 0.153813 | -0.706378 | -0.700799 | -0.698006 | -0.703979 | -0.705836 | 0.903281 | 0.903281 | 0.903281 | 0.904368 | 0.911966 | 0.911966 | 0.911966 | 0.904368 | 0.898125 | 0.898125 | 0.898125 | 0.887381 | 0.835864 | 0.835864 | 0.835864 | 0.887381 | 0.668715 | 0.616742 | 0.616742 | 0.616742 | 0.668715 | 0.612659 | 0.486627 | 0.486627 | 0.486627 | 0.612659 |
| mentality_vision | 0.160532 | -0.307677 | -0.253481 | 0.454645 | 0.380165 | 0.294718 | 0.228296 | 0.302370 | 0.514639 | -0.074602 | -0.017567 | 0.435799 | 0.632900 | 0.657614 | 0.606899 | 0.182386 | 0.348580 | -0.368787 | 0.590592 | 0.595582 | 0.269583 | 0.640776 | 0.602356 | 0.623687 | 0.635218 | 0.618790 | 0.611578 | 0.628627 | 0.366858 | 0.333093 | 0.485716 | 0.429489 | 0.376408 | 0.567777 | 0.034678 | 0.397458 | -0.053100 | 0.647124 | 0.276910 | 0.160827 | 0.631142 | 1.000000 | 0.553786 | 0.074908 | 0.128337 | 0.090808 | -0.339098 | -0.331159 | -0.329005 | -0.328355 | -0.336131 | 0.588812 | 0.588812 | 0.588812 | 0.624646 | 0.628434 | 0.628434 | 0.628434 | 0.624646 | 0.643983 | 0.643983 | 0.643983 | 0.616911 | 0.614825 | 0.614825 | 0.614825 | 0.616911 | 0.446975 | 0.431218 | 0.431218 | 0.431218 | 0.446975 | 0.397495 | 0.293680 | 0.293680 | 0.293680 | 0.397495 |
| mentality_penalties | 0.071897 | -0.320547 | -0.265101 | 0.288142 | 0.251420 | 0.201538 | 0.181447 | 0.316899 | 0.646602 | -0.005210 | -0.004697 | 0.662849 | 0.862874 | 0.752145 | 0.776331 | 0.262622 | 0.595183 | -0.644618 | 0.663582 | 0.813355 | 0.571513 | 0.663619 | 0.795701 | 0.760631 | 0.746328 | 0.738011 | 0.550824 | 0.758144 | 0.459133 | 0.448614 | 0.494055 | 0.257361 | 0.386284 | 0.772706 | 0.070303 | 0.484088 | 0.021208 | 0.789812 | 0.344994 | 0.127833 | 0.786006 | 0.553786 | 1.000000 | 0.104479 | 0.156818 | 0.114689 | -0.644510 | -0.640010 | -0.637325 | -0.642691 | -0.645470 | 0.819145 | 0.819145 | 0.819145 | 0.799166 | 0.812367 | 0.812367 | 0.812367 | 0.799166 | 0.799480 | 0.799480 | 0.799480 | 0.781754 | 0.746818 | 0.746818 | 0.746818 | 0.781754 | 0.585662 | 0.552169 | 0.552169 | 0.552169 | 0.585662 | 0.537874 | 0.440939 | 0.440939 | 0.440939 | 0.537874 |
| defending_marking | 0.095487 | -0.044097 | -0.051303 | 0.199397 | 0.129611 | 0.043116 | 0.050547 | 0.025367 | 0.042260 | -0.103717 | 0.003763 | 0.373159 | 0.141720 | 0.471315 | 0.355289 | 0.950341 | 0.644740 | -0.514756 | 0.405365 | -0.029025 | 0.553592 | 0.450958 | 0.062621 | 0.262768 | 0.246658 | 0.262319 | 0.504891 | 0.357393 | 0.080902 | 0.105869 | 0.038587 | 0.139241 | 0.079182 | 0.266954 | 0.189044 | 0.496106 | 0.286243 | 0.159072 | 0.680253 | 0.876480 | 0.134477 | 0.074908 | 0.104479 | 1.000000 | 0.946202 | 0.947263 | -0.512059 | -0.511903 | -0.508248 | -0.509167 | -0.513108 | 0.343647 | 0.343647 | 0.343647 | 0.349419 | 0.338706 | 0.338706 | 0.338706 | 0.349419 | 0.369532 | 0.369532 | 0.369532 | 0.401499 | 0.519940 | 0.519940 | 0.519940 | 0.401499 | 0.749758 | 0.791030 | 0.791030 | 0.791030 | 0.749758 | 0.797458 | 0.868021 | 0.868021 | 0.868021 | 0.797458 |
| defending_standing_tackle | 0.085014 | -0.052524 | -0.063893 | 0.224607 | 0.162444 | 0.075388 | 0.073716 | 0.050568 | 0.097889 | -0.102330 | 0.001320 | 0.414313 | 0.201477 | 0.526870 | 0.408721 | 0.962533 | 0.685592 | -0.556996 | 0.448174 | 0.026011 | 0.582708 | 0.505908 | 0.118518 | 0.315544 | 0.301985 | 0.318619 | 0.550589 | 0.410996 | 0.106630 | 0.131338 | 0.067829 | 0.160398 | 0.097653 | 0.320677 | 0.185032 | 0.529711 | 0.295564 | 0.216314 | 0.709337 | 0.890864 | 0.192662 | 0.128337 | 0.156818 | 0.946202 | 1.000000 | 0.959425 | -0.553528 | -0.552902 | -0.549392 | -0.550899 | -0.555346 | 0.399781 | 0.399781 | 0.399781 | 0.405141 | 0.396203 | 0.396203 | 0.396203 | 0.405141 | 0.427256 | 0.427256 | 0.427256 | 0.456293 | 0.576335 | 0.576335 | 0.576335 | 0.456293 | 0.788744 | 0.830142 | 0.830142 | 0.830142 | 0.788744 | 0.833216 | 0.896043 | 0.896043 | 0.896043 | 0.833216 |
| defending_sliding_tackle | 0.068744 | -0.071170 | -0.081469 | 0.191321 | 0.139049 | 0.056511 | 0.061860 | 0.030081 | 0.061889 | -0.090515 | 0.009542 | 0.395538 | 0.154064 | 0.488785 | 0.375334 | 0.946758 | 0.643789 | -0.526258 | 0.427599 | -0.020106 | 0.543606 | 0.462825 | 0.076556 | 0.284408 | 0.268331 | 0.280936 | 0.517332 | 0.371727 | 0.110050 | 0.132122 | 0.067121 | 0.133325 | 0.111921 | 0.276584 | 0.192103 | 0.503195 | 0.255583 | 0.171608 | 0.678957 | 0.873848 | 0.153813 | 0.090808 | 0.114689 | 0.947263 | 0.959425 | 1.000000 | -0.524035 | -0.522774 | -0.519861 | -0.520562 | -0.525231 | 0.356257 | 0.356257 | 0.356257 | 0.368155 | 0.355418 | 0.355418 | 0.355418 | 0.368155 | 0.386849 | 0.386849 | 0.386849 | 0.419834 | 0.534961 | 0.534961 | 0.534961 | 0.419834 | 0.764351 | 0.798299 | 0.798299 | 0.798299 | 0.764351 | 0.811201 | 0.870404 | 0.870404 | 0.870404 | 0.811201 |
| goalkeeping_diving | 0.110753 | 0.345080 | 0.337671 | -0.013305 | -0.054610 | -0.011160 | 0.008567 | -0.229314 | -0.603506 | -0.000528 | -0.010811 | -0.873950 | -0.758078 | -0.842429 | -0.860783 | -0.668346 | -0.886312 | 0.959659 | -0.706749 | -0.627670 | -0.787212 | -0.694399 | -0.629848 | -0.772031 | -0.661427 | -0.628741 | -0.593282 | -0.784465 | -0.493847 | -0.495886 | -0.442482 | -0.000527 | -0.401418 | -0.697839 | -0.101529 | -0.642126 | -0.084551 | -0.654598 | -0.558962 | -0.453764 | -0.706378 | -0.339098 | -0.644510 | -0.512059 | -0.553528 | -0.524035 | 1.000000 | 0.965109 | 0.961118 | 0.965527 | 0.972006 | -0.876228 | -0.876228 | -0.876228 | -0.864253 | -0.864122 | -0.864122 | -0.864122 | -0.864253 | -0.864870 | -0.864870 | -0.864870 | -0.876887 | -0.880697 | -0.880697 | -0.880697 | -0.876887 | -0.873948 | -0.849979 | -0.849979 | -0.849979 | -0.873948 | -0.859059 | -0.806065 | -0.806065 | -0.806065 | -0.859059 |
| goalkeeping_handling | 0.118913 | 0.344673 | 0.339249 | -0.008285 | -0.052694 | -0.010169 | 0.008862 | -0.225646 | -0.598528 | -0.006068 | -0.011434 | -0.868274 | -0.752263 | -0.836956 | -0.855039 | -0.666287 | -0.881632 | 0.951587 | -0.702701 | -0.622927 | -0.784757 | -0.689938 | -0.625313 | -0.766865 | -0.656669 | -0.623520 | -0.588155 | -0.780271 | -0.491974 | -0.495793 | -0.440033 | 0.002699 | -0.399526 | -0.693702 | -0.101283 | -0.639228 | -0.081549 | -0.650759 | -0.555333 | -0.453750 | -0.700799 | -0.331159 | -0.640010 | -0.511903 | -0.552902 | -0.522774 | 0.965109 | 1.000000 | 0.960160 | 0.965378 | 0.966128 | -0.870298 | -0.870298 | -0.870298 | -0.858322 | -0.858170 | -0.858170 | -0.858170 | -0.858322 | -0.858960 | -0.858960 | -0.858960 | -0.871058 | -0.875173 | -0.875173 | -0.875173 | -0.871058 | -0.869498 | -0.845903 | -0.845903 | -0.845903 | -0.869498 | -0.854817 | -0.802532 | -0.802532 | -0.802532 | -0.854817 |
| goalkeeping_kicking | 0.114074 | 0.340714 | 0.334518 | -0.011916 | -0.054882 | -0.012252 | 0.007609 | -0.227129 | -0.598083 | -0.005026 | -0.012459 | -0.865116 | -0.749882 | -0.834337 | -0.852224 | -0.662787 | -0.879163 | 0.950879 | -0.699935 | -0.619716 | -0.779547 | -0.686612 | -0.621765 | -0.763112 | -0.654681 | -0.620866 | -0.585491 | -0.776465 | -0.488022 | -0.490757 | -0.439042 | -0.000727 | -0.395503 | -0.690823 | -0.099919 | -0.636662 | -0.084190 | -0.646422 | -0.553184 | -0.448732 | -0.698006 | -0.329005 | -0.637325 | -0.508248 | -0.549392 | -0.519861 | 0.961118 | 0.960160 | 1.000000 | 0.959859 | 0.962098 | -0.867379 | -0.867379 | -0.867379 | -0.855536 | -0.855347 | -0.855347 | -0.855347 | -0.855536 | -0.856093 | -0.856093 | -0.856093 | -0.868190 | -0.871984 | -0.871984 | -0.871984 | -0.868190 | -0.865872 | -0.842329 | -0.842329 | -0.842329 | -0.865872 | -0.851173 | -0.799097 | -0.799097 | -0.799097 | -0.851173 |
| goalkeeping_positioning | 0.126850 | 0.346370 | 0.340144 | -0.004463 | -0.053324 | -0.011187 | 0.010341 | -0.227467 | -0.599330 | -0.006368 | -0.013338 | -0.868614 | -0.753777 | -0.837036 | -0.855846 | -0.662892 | -0.879946 | 0.950258 | -0.703763 | -0.626061 | -0.782220 | -0.689461 | -0.627386 | -0.769358 | -0.660964 | -0.627084 | -0.588310 | -0.779936 | -0.493843 | -0.497843 | -0.442835 | 0.005807 | -0.401498 | -0.694882 | -0.099698 | -0.638767 | -0.078650 | -0.651769 | -0.551825 | -0.449937 | -0.703979 | -0.328355 | -0.642691 | -0.509167 | -0.550899 | -0.520562 | 0.965527 | 0.965378 | 0.959859 | 1.000000 | 0.966918 | -0.870844 | -0.870844 | -0.870844 | -0.859059 | -0.858909 | -0.858909 | -0.858909 | -0.859059 | -0.859540 | -0.859540 | -0.859540 | -0.871543 | -0.874876 | -0.874876 | -0.874876 | -0.871543 | -0.867852 | -0.843831 | -0.843831 | -0.843831 | -0.867852 | -0.852858 | -0.799867 | -0.799867 | -0.799867 | -0.852858 |
| goalkeeping_reflexes | 0.109512 | 0.343593 | 0.334678 | -0.013170 | -0.053684 | -0.011180 | 0.007519 | -0.231161 | -0.602399 | -0.001365 | -0.010358 | -0.873302 | -0.757140 | -0.841635 | -0.859972 | -0.669583 | -0.887810 | 0.958423 | -0.705759 | -0.626086 | -0.787717 | -0.693534 | -0.628249 | -0.771108 | -0.660563 | -0.626592 | -0.591246 | -0.784129 | -0.493340 | -0.496280 | -0.441984 | -0.001361 | -0.399564 | -0.696986 | -0.102472 | -0.643495 | -0.087483 | -0.653411 | -0.560739 | -0.455025 | -0.705836 | -0.336131 | -0.645470 | -0.513108 | -0.555346 | -0.525231 | 0.972006 | 0.966128 | 0.962098 | 0.966918 | 1.000000 | -0.875834 | -0.875834 | -0.875834 | -0.863498 | -0.863480 | -0.863480 | -0.863480 | -0.863498 | -0.864134 | -0.864134 | -0.864134 | -0.876241 | -0.880201 | -0.880201 | -0.880201 | -0.876241 | -0.874259 | -0.850509 | -0.850509 | -0.850509 | -0.874259 | -0.859623 | -0.807174 | -0.807174 | -0.807174 | -0.859623 |
| ls | 0.022931 | -0.371797 | -0.325837 | 0.321237 | 0.297491 | 0.206180 | 0.148768 | 0.338631 | 0.736327 | -0.031245 | -0.000028 | 0.895389 | 0.958163 | 0.940656 | 0.969502 | 0.526746 | 0.848474 | -0.882866 | 0.822018 | 0.859337 | 0.775686 | 0.829176 | 0.843690 | 0.919107 | 0.829595 | 0.780259 | 0.704655 | 0.926808 | 0.604166 | 0.603702 | 0.594003 | 0.284001 | 0.459218 | 0.876177 | 0.139095 | 0.688676 | 0.098058 | 0.867660 | 0.554021 | 0.346869 | 0.903281 | 0.588812 | 0.819145 | 0.343647 | 0.399781 | 0.356257 | -0.876228 | -0.870298 | -0.867379 | -0.870844 | -0.875834 | 1.000000 | 1.000000 | 1.000000 | 0.985759 | 0.993289 | 0.993289 | 0.993289 | 0.985759 | 0.985274 | 0.985274 | 0.985274 | 0.981555 | 0.959796 | 0.959796 | 0.959796 | 0.981555 | 0.850656 | 0.814169 | 0.814169 | 0.814169 | 0.850656 | 0.809747 | 0.713309 | 0.713309 | 0.713309 | 0.809747 |
| st | 0.022931 | -0.371797 | -0.325837 | 0.321237 | 0.297491 | 0.206180 | 0.148768 | 0.338631 | 0.736327 | -0.031245 | -0.000028 | 0.895389 | 0.958163 | 0.940656 | 0.969502 | 0.526746 | 0.848474 | -0.882866 | 0.822018 | 0.859337 | 0.775686 | 0.829176 | 0.843690 | 0.919107 | 0.829595 | 0.780259 | 0.704655 | 0.926808 | 0.604166 | 0.603702 | 0.594003 | 0.284001 | 0.459218 | 0.876177 | 0.139095 | 0.688676 | 0.098058 | 0.867660 | 0.554021 | 0.346869 | 0.903281 | 0.588812 | 0.819145 | 0.343647 | 0.399781 | 0.356257 | -0.876228 | -0.870298 | -0.867379 | -0.870844 | -0.875834 | 1.000000 | 1.000000 | 1.000000 | 0.985759 | 0.993289 | 0.993289 | 0.993289 | 0.985759 | 0.985274 | 0.985274 | 0.985274 | 0.981555 | 0.959796 | 0.959796 | 0.959796 | 0.981555 | 0.850656 | 0.814169 | 0.814169 | 0.814169 | 0.850656 | 0.809747 | 0.713309 | 0.713309 | 0.713309 | 0.809747 |
| rs | 0.022931 | -0.371797 | -0.325837 | 0.321237 | 0.297491 | 0.206180 | 0.148768 | 0.338631 | 0.736327 | -0.031245 | -0.000028 | 0.895389 | 0.958163 | 0.940656 | 0.969502 | 0.526746 | 0.848474 | -0.882866 | 0.822018 | 0.859337 | 0.775686 | 0.829176 | 0.843690 | 0.919107 | 0.829595 | 0.780259 | 0.704655 | 0.926808 | 0.604166 | 0.603702 | 0.594003 | 0.284001 | 0.459218 | 0.876177 | 0.139095 | 0.688676 | 0.098058 | 0.867660 | 0.554021 | 0.346869 | 0.903281 | 0.588812 | 0.819145 | 0.343647 | 0.399781 | 0.356257 | -0.876228 | -0.870298 | -0.867379 | -0.870844 | -0.875834 | 1.000000 | 1.000000 | 1.000000 | 0.985759 | 0.993289 | 0.993289 | 0.993289 | 0.985759 | 0.985274 | 0.985274 | 0.985274 | 0.981555 | 0.959796 | 0.959796 | 0.959796 | 0.981555 | 0.850656 | 0.814169 | 0.814169 | 0.814169 | 0.850656 | 0.809747 | 0.713309 | 0.713309 | 0.713309 | 0.809747 |
| lw | -0.003591 | -0.455301 | -0.407746 | 0.314071 | 0.306260 | 0.207746 | 0.139487 | 0.344032 | 0.757659 | -0.026383 | 0.004863 | 0.916727 | 0.938259 | 0.965312 | 0.992113 | 0.523097 | 0.808134 | -0.870797 | 0.875131 | 0.838406 | 0.702986 | 0.853626 | 0.829032 | 0.950743 | 0.857413 | 0.800120 | 0.741146 | 0.943087 | 0.661794 | 0.647820 | 0.661655 | 0.281018 | 0.537243 | 0.844244 | 0.118867 | 0.695161 | 0.004797 | 0.864848 | 0.526201 | 0.354886 | 0.904368 | 0.624646 | 0.799166 | 0.349419 | 0.405141 | 0.368155 | -0.864253 | -0.858322 | -0.855536 | -0.859059 | -0.863498 | 0.985759 | 0.985759 | 0.985759 | 1.000000 | 0.997293 | 0.997293 | 0.997293 | 1.000000 | 0.996731 | 0.996731 | 0.996731 | 0.997319 | 0.970024 | 0.970024 | 0.970024 | 0.997319 | 0.863179 | 0.816256 | 0.816256 | 0.816256 | 0.863179 | 0.817004 | 0.699402 | 0.699402 | 0.699402 | 0.817004 |
| lf | 0.009109 | -0.426990 | -0.379890 | 0.324193 | 0.310361 | 0.213569 | 0.148277 | 0.346500 | 0.753949 | -0.028186 | 0.003806 | 0.902720 | 0.951668 | 0.960350 | 0.986707 | 0.516990 | 0.816910 | -0.870610 | 0.851572 | 0.852345 | 0.723562 | 0.852871 | 0.840686 | 0.943775 | 0.852536 | 0.799696 | 0.735400 | 0.943181 | 0.635606 | 0.624833 | 0.636283 | 0.292871 | 0.510825 | 0.862492 | 0.120011 | 0.689726 | 0.032616 | 0.874767 | 0.533836 | 0.347690 | 0.911966 | 0.628434 | 0.812367 | 0.338706 | 0.396203 | 0.355418 | -0.864122 | -0.858170 | -0.855347 | -0.858909 | -0.863480 | 0.993289 | 0.993289 | 0.993289 | 0.997293 | 1.000000 | 1.000000 | 1.000000 | 0.997293 | 0.997128 | 0.997128 | 0.997128 | 0.993663 | 0.970026 | 0.970026 | 0.970026 | 0.993663 | 0.854643 | 0.813408 | 0.813408 | 0.813408 | 0.854643 | 0.809238 | 0.698344 | 0.698344 | 0.698344 | 0.809238 |
| cf | 0.009109 | -0.426990 | -0.379890 | 0.324193 | 0.310361 | 0.213569 | 0.148277 | 0.346500 | 0.753949 | -0.028186 | 0.003806 | 0.902720 | 0.951668 | 0.960350 | 0.986707 | 0.516990 | 0.816910 | -0.870610 | 0.851572 | 0.852345 | 0.723562 | 0.852871 | 0.840686 | 0.943775 | 0.852536 | 0.799696 | 0.735400 | 0.943181 | 0.635606 | 0.624833 | 0.636283 | 0.292871 | 0.510825 | 0.862492 | 0.120011 | 0.689726 | 0.032616 | 0.874767 | 0.533836 | 0.347690 | 0.911966 | 0.628434 | 0.812367 | 0.338706 | 0.396203 | 0.355418 | -0.864122 | -0.858170 | -0.855347 | -0.858909 | -0.863480 | 0.993289 | 0.993289 | 0.993289 | 0.997293 | 1.000000 | 1.000000 | 1.000000 | 0.997293 | 0.997128 | 0.997128 | 0.997128 | 0.993663 | 0.970026 | 0.970026 | 0.970026 | 0.993663 | 0.854643 | 0.813408 | 0.813408 | 0.813408 | 0.854643 | 0.809238 | 0.698344 | 0.698344 | 0.698344 | 0.809238 |
| rf | 0.009109 | -0.426990 | -0.379890 | 0.324193 | 0.310361 | 0.213569 | 0.148277 | 0.346500 | 0.753949 | -0.028186 | 0.003806 | 0.902720 | 0.951668 | 0.960350 | 0.986707 | 0.516990 | 0.816910 | -0.870610 | 0.851572 | 0.852345 | 0.723562 | 0.852871 | 0.840686 | 0.943775 | 0.852536 | 0.799696 | 0.735400 | 0.943181 | 0.635606 | 0.624833 | 0.636283 | 0.292871 | 0.510825 | 0.862492 | 0.120011 | 0.689726 | 0.032616 | 0.874767 | 0.533836 | 0.347690 | 0.911966 | 0.628434 | 0.812367 | 0.338706 | 0.396203 | 0.355418 | -0.864122 | -0.858170 | -0.855347 | -0.858909 | -0.863480 | 0.993289 | 0.993289 | 0.993289 | 0.997293 | 1.000000 | 1.000000 | 1.000000 | 0.997293 | 0.997128 | 0.997128 | 0.997128 | 0.993663 | 0.970026 | 0.970026 | 0.970026 | 0.993663 | 0.854643 | 0.813408 | 0.813408 | 0.813408 | 0.854643 | 0.809238 | 0.698344 | 0.698344 | 0.698344 | 0.809238 |
| rw | -0.003591 | -0.455301 | -0.407746 | 0.314071 | 0.306260 | 0.207746 | 0.139487 | 0.344032 | 0.757659 | -0.026383 | 0.004863 | 0.916727 | 0.938259 | 0.965312 | 0.992113 | 0.523097 | 0.808134 | -0.870797 | 0.875131 | 0.838406 | 0.702986 | 0.853626 | 0.829032 | 0.950743 | 0.857413 | 0.800120 | 0.741146 | 0.943087 | 0.661794 | 0.647820 | 0.661655 | 0.281018 | 0.537243 | 0.844244 | 0.118867 | 0.695161 | 0.004797 | 0.864848 | 0.526201 | 0.354886 | 0.904368 | 0.624646 | 0.799166 | 0.349419 | 0.405141 | 0.368155 | -0.864253 | -0.858322 | -0.855536 | -0.859059 | -0.863498 | 0.985759 | 0.985759 | 0.985759 | 1.000000 | 0.997293 | 0.997293 | 0.997293 | 1.000000 | 0.996731 | 0.996731 | 0.996731 | 0.997319 | 0.970024 | 0.970024 | 0.970024 | 0.997319 | 0.863179 | 0.816256 | 0.816256 | 0.816256 | 0.863179 | 0.817004 | 0.699402 | 0.699402 | 0.699402 | 0.817004 |
| lam | 0.013235 | -0.438802 | -0.392627 | 0.328978 | 0.313618 | 0.214946 | 0.149761 | 0.345704 | 0.749065 | -0.033223 | 0.003808 | 0.897215 | 0.936173 | 0.975713 | 0.988354 | 0.543815 | 0.817815 | -0.871464 | 0.862944 | 0.830024 | 0.714178 | 0.875312 | 0.826355 | 0.942851 | 0.856633 | 0.807038 | 0.768470 | 0.947908 | 0.626725 | 0.612193 | 0.640666 | 0.295602 | 0.521549 | 0.850102 | 0.115986 | 0.695555 | 0.023525 | 0.868562 | 0.545512 | 0.380442 | 0.898125 | 0.643983 | 0.799480 | 0.369532 | 0.427256 | 0.386849 | -0.864870 | -0.858960 | -0.856093 | -0.859540 | -0.864134 | 0.985274 | 0.985274 | 0.985274 | 0.996731 | 0.997128 | 0.997128 | 0.997128 | 0.996731 | 1.000000 | 1.000000 | 1.000000 | 0.996390 | 0.981111 | 0.981111 | 0.981111 | 0.996390 | 0.871561 | 0.834151 | 0.834151 | 0.834151 | 0.871561 | 0.827104 | 0.716930 | 0.716930 | 0.716930 | 0.827104 |
| cam | 0.013235 | -0.438802 | -0.392627 | 0.328978 | 0.313618 | 0.214946 | 0.149761 | 0.345704 | 0.749065 | -0.033223 | 0.003808 | 0.897215 | 0.936173 | 0.975713 | 0.988354 | 0.543815 | 0.817815 | -0.871464 | 0.862944 | 0.830024 | 0.714178 | 0.875312 | 0.826355 | 0.942851 | 0.856633 | 0.807038 | 0.768470 | 0.947908 | 0.626725 | 0.612193 | 0.640666 | 0.295602 | 0.521549 | 0.850102 | 0.115986 | 0.695555 | 0.023525 | 0.868562 | 0.545512 | 0.380442 | 0.898125 | 0.643983 | 0.799480 | 0.369532 | 0.427256 | 0.386849 | -0.864870 | -0.858960 | -0.856093 | -0.859540 | -0.864134 | 0.985274 | 0.985274 | 0.985274 | 0.996731 | 0.997128 | 0.997128 | 0.997128 | 0.996731 | 1.000000 | 1.000000 | 1.000000 | 0.996390 | 0.981111 | 0.981111 | 0.981111 | 0.996390 | 0.871561 | 0.834151 | 0.834151 | 0.834151 | 0.871561 | 0.827104 | 0.716930 | 0.716930 | 0.716930 | 0.827104 |
| ram | 0.013235 | -0.438802 | -0.392627 | 0.328978 | 0.313618 | 0.214946 | 0.149761 | 0.345704 | 0.749065 | -0.033223 | 0.003808 | 0.897215 | 0.936173 | 0.975713 | 0.988354 | 0.543815 | 0.817815 | -0.871464 | 0.862944 | 0.830024 | 0.714178 | 0.875312 | 0.826355 | 0.942851 | 0.856633 | 0.807038 | 0.768470 | 0.947908 | 0.626725 | 0.612193 | 0.640666 | 0.295602 | 0.521549 | 0.850102 | 0.115986 | 0.695555 | 0.023525 | 0.868562 | 0.545512 | 0.380442 | 0.898125 | 0.643983 | 0.799480 | 0.369532 | 0.427256 | 0.386849 | -0.864870 | -0.858960 | -0.856093 | -0.859540 | -0.864134 | 0.985274 | 0.985274 | 0.985274 | 0.996731 | 0.997128 | 0.997128 | 0.997128 | 0.996731 | 1.000000 | 1.000000 | 1.000000 | 0.996390 | 0.981111 | 0.981111 | 0.981111 | 0.996390 | 0.871561 | 0.834151 | 0.834151 | 0.834151 | 0.871561 | 0.827104 | 0.716930 | 0.716930 | 0.716930 | 0.827104 |
| lm | 0.001210 | -0.445813 | -0.400793 | 0.319067 | 0.306790 | 0.205865 | 0.138593 | 0.335959 | 0.741494 | -0.034475 | 0.005199 | 0.918541 | 0.918355 | 0.975841 | 0.990349 | 0.571736 | 0.832759 | -0.883713 | 0.881982 | 0.807571 | 0.719612 | 0.867623 | 0.807050 | 0.943085 | 0.850916 | 0.795987 | 0.765193 | 0.943578 | 0.649924 | 0.638277 | 0.643769 | 0.282749 | 0.528261 | 0.836326 | 0.126938 | 0.720161 | 0.024305 | 0.846400 | 0.558113 | 0.404170 | 0.887381 | 0.616911 | 0.781754 | 0.401499 | 0.456293 | 0.419834 | -0.876887 | -0.871058 | -0.868190 | -0.871543 | -0.876241 | 0.981555 | 0.981555 | 0.981555 | 0.997319 | 0.993663 | 0.993663 | 0.993663 | 0.997319 | 0.996390 | 0.996390 | 0.996390 | 1.000000 | 0.982114 | 0.982114 | 0.982114 | 1.000000 | 0.892433 | 0.849919 | 0.849919 | 0.849919 | 0.892433 | 0.850105 | 0.739007 | 0.739007 | 0.739007 | 0.850105 |
| lcm | 0.050825 | -0.391435 | -0.350573 | 0.355663 | 0.317066 | 0.214490 | 0.159101 | 0.321143 | 0.684234 | -0.058140 | 0.000444 | 0.868593 | 0.876037 | 0.987252 | 0.962021 | 0.683010 | 0.874412 | -0.887769 | 0.859988 | 0.739998 | 0.758542 | 0.900025 | 0.760627 | 0.898746 | 0.827431 | 0.791672 | 0.823421 | 0.933271 | 0.554812 | 0.547184 | 0.570597 | 0.313722 | 0.473680 | 0.828000 | 0.140109 | 0.737206 | 0.093435 | 0.822542 | 0.639390 | 0.530829 | 0.835864 | 0.614825 | 0.746818 | 0.519940 | 0.576335 | 0.534961 | -0.880697 | -0.875173 | -0.871984 | -0.874876 | -0.880201 | 0.959796 | 0.959796 | 0.959796 | 0.970024 | 0.970026 | 0.970026 | 0.970026 | 0.970024 | 0.981111 | 0.981111 | 0.981111 | 0.982114 | 1.000000 | 1.000000 | 1.000000 | 0.982114 | 0.939707 | 0.920290 | 0.920290 | 0.920290 | 0.939707 | 0.908444 | 0.825073 | 0.825073 | 0.825073 | 0.908444 |
| cm | 0.050825 | -0.391435 | -0.350573 | 0.355663 | 0.317066 | 0.214490 | 0.159101 | 0.321143 | 0.684234 | -0.058140 | 0.000444 | 0.868593 | 0.876037 | 0.987252 | 0.962021 | 0.683010 | 0.874412 | -0.887769 | 0.859988 | 0.739998 | 0.758542 | 0.900025 | 0.760627 | 0.898746 | 0.827431 | 0.791672 | 0.823421 | 0.933271 | 0.554812 | 0.547184 | 0.570597 | 0.313722 | 0.473680 | 0.828000 | 0.140109 | 0.737206 | 0.093435 | 0.822542 | 0.639390 | 0.530829 | 0.835864 | 0.614825 | 0.746818 | 0.519940 | 0.576335 | 0.534961 | -0.880697 | -0.875173 | -0.871984 | -0.874876 | -0.880201 | 0.959796 | 0.959796 | 0.959796 | 0.970024 | 0.970026 | 0.970026 | 0.970026 | 0.970024 | 0.981111 | 0.981111 | 0.981111 | 0.982114 | 1.000000 | 1.000000 | 1.000000 | 0.982114 | 0.939707 | 0.920290 | 0.920290 | 0.920290 | 0.939707 | 0.908444 | 0.825073 | 0.825073 | 0.825073 | 0.908444 |
| rcm | 0.050825 | -0.391435 | -0.350573 | 0.355663 | 0.317066 | 0.214490 | 0.159101 | 0.321143 | 0.684234 | -0.058140 | 0.000444 | 0.868593 | 0.876037 | 0.987252 | 0.962021 | 0.683010 | 0.874412 | -0.887769 | 0.859988 | 0.739998 | 0.758542 | 0.900025 | 0.760627 | 0.898746 | 0.827431 | 0.791672 | 0.823421 | 0.933271 | 0.554812 | 0.547184 | 0.570597 | 0.313722 | 0.473680 | 0.828000 | 0.140109 | 0.737206 | 0.093435 | 0.822542 | 0.639390 | 0.530829 | 0.835864 | 0.614825 | 0.746818 | 0.519940 | 0.576335 | 0.534961 | -0.880697 | -0.875173 | -0.871984 | -0.874876 | -0.880201 | 0.959796 | 0.959796 | 0.959796 | 0.970024 | 0.970026 | 0.970026 | 0.970026 | 0.970024 | 0.981111 | 0.981111 | 0.981111 | 0.982114 | 1.000000 | 1.000000 | 1.000000 | 0.982114 | 0.939707 | 0.920290 | 0.920290 | 0.920290 | 0.939707 | 0.908444 | 0.825073 | 0.825073 | 0.825073 | 0.908444 |
| rm | 0.001210 | -0.445813 | -0.400793 | 0.319067 | 0.306790 | 0.205865 | 0.138593 | 0.335959 | 0.741494 | -0.034475 | 0.005199 | 0.918541 | 0.918355 | 0.975841 | 0.990349 | 0.571736 | 0.832759 | -0.883713 | 0.881982 | 0.807571 | 0.719612 | 0.867623 | 0.807050 | 0.943085 | 0.850916 | 0.795987 | 0.765193 | 0.943578 | 0.649924 | 0.638277 | 0.643769 | 0.282749 | 0.528261 | 0.836326 | 0.126938 | 0.720161 | 0.024305 | 0.846400 | 0.558113 | 0.404170 | 0.887381 | 0.616911 | 0.781754 | 0.401499 | 0.456293 | 0.419834 | -0.876887 | -0.871058 | -0.868190 | -0.871543 | -0.876241 | 0.981555 | 0.981555 | 0.981555 | 0.997319 | 0.993663 | 0.993663 | 0.993663 | 0.997319 | 0.996390 | 0.996390 | 0.996390 | 1.000000 | 0.982114 | 0.982114 | 0.982114 | 1.000000 | 0.892433 | 0.849919 | 0.849919 | 0.849919 | 0.892433 | 0.850105 | 0.739007 | 0.739007 | 0.739007 | 0.850105 |
| lwb | 0.046002 | -0.325603 | -0.298411 | 0.314825 | 0.270101 | 0.164308 | 0.122380 | 0.239683 | 0.531699 | -0.078242 | 0.001424 | 0.832935 | 0.698758 | 0.908033 | 0.860507 | 0.875960 | 0.915032 | -0.881761 | 0.808197 | 0.528117 | 0.781133 | 0.817747 | 0.577657 | 0.772177 | 0.702371 | 0.672190 | 0.769808 | 0.824830 | 0.493498 | 0.501434 | 0.463525 | 0.260861 | 0.400874 | 0.705215 | 0.196337 | 0.764014 | 0.168889 | 0.652589 | 0.734497 | 0.728475 | 0.668715 | 0.446975 | 0.585662 | 0.749758 | 0.788744 | 0.764351 | -0.873948 | -0.869498 | -0.865872 | -0.867852 | -0.874259 | 0.850656 | 0.850656 | 0.850656 | 0.863179 | 0.854643 | 0.854643 | 0.854643 | 0.863179 | 0.871561 | 0.871561 | 0.871561 | 0.892433 | 0.939707 | 0.939707 | 0.939707 | 0.892433 | 1.000000 | 0.989661 | 0.989661 | 0.989661 | 1.000000 | 0.995527 | 0.954171 | 0.954171 | 0.954171 | 0.995527 |
| ldm | 0.084582 | -0.258504 | -0.233983 | 0.331386 | 0.267885 | 0.164751 | 0.134257 | 0.223933 | 0.478510 | -0.092148 | -0.003559 | 0.764888 | 0.656536 | 0.883237 | 0.812096 | 0.911355 | 0.920885 | -0.857812 | 0.751275 | 0.477998 | 0.794573 | 0.813068 | 0.535637 | 0.715617 | 0.659306 | 0.645073 | 0.781576 | 0.792925 | 0.398022 | 0.408964 | 0.385108 | 0.273106 | 0.332768 | 0.684018 | 0.196919 | 0.736461 | 0.233944 | 0.617293 | 0.775185 | 0.776615 | 0.616742 | 0.431218 | 0.552169 | 0.791030 | 0.830142 | 0.798299 | -0.849979 | -0.845903 | -0.842329 | -0.843831 | -0.850509 | 0.814169 | 0.814169 | 0.814169 | 0.816256 | 0.813408 | 0.813408 | 0.813408 | 0.816256 | 0.834151 | 0.834151 | 0.834151 | 0.849919 | 0.920290 | 0.920290 | 0.920290 | 0.849919 | 0.989661 | 1.000000 | 1.000000 | 1.000000 | 0.989661 | 0.992773 | 0.976520 | 0.976520 | 0.976520 | 0.992773 |
| cdm | 0.084582 | -0.258504 | -0.233983 | 0.331386 | 0.267885 | 0.164751 | 0.134257 | 0.223933 | 0.478510 | -0.092148 | -0.003559 | 0.764888 | 0.656536 | 0.883237 | 0.812096 | 0.911355 | 0.920885 | -0.857812 | 0.751275 | 0.477998 | 0.794573 | 0.813068 | 0.535637 | 0.715617 | 0.659306 | 0.645073 | 0.781576 | 0.792925 | 0.398022 | 0.408964 | 0.385108 | 0.273106 | 0.332768 | 0.684018 | 0.196919 | 0.736461 | 0.233944 | 0.617293 | 0.775185 | 0.776615 | 0.616742 | 0.431218 | 0.552169 | 0.791030 | 0.830142 | 0.798299 | -0.849979 | -0.845903 | -0.842329 | -0.843831 | -0.850509 | 0.814169 | 0.814169 | 0.814169 | 0.816256 | 0.813408 | 0.813408 | 0.813408 | 0.816256 | 0.834151 | 0.834151 | 0.834151 | 0.849919 | 0.920290 | 0.920290 | 0.920290 | 0.849919 | 0.989661 | 1.000000 | 1.000000 | 1.000000 | 0.989661 | 0.992773 | 0.976520 | 0.976520 | 0.976520 | 0.992773 |
| rdm | 0.084582 | -0.258504 | -0.233983 | 0.331386 | 0.267885 | 0.164751 | 0.134257 | 0.223933 | 0.478510 | -0.092148 | -0.003559 | 0.764888 | 0.656536 | 0.883237 | 0.812096 | 0.911355 | 0.920885 | -0.857812 | 0.751275 | 0.477998 | 0.794573 | 0.813068 | 0.535637 | 0.715617 | 0.659306 | 0.645073 | 0.781576 | 0.792925 | 0.398022 | 0.408964 | 0.385108 | 0.273106 | 0.332768 | 0.684018 | 0.196919 | 0.736461 | 0.233944 | 0.617293 | 0.775185 | 0.776615 | 0.616742 | 0.431218 | 0.552169 | 0.791030 | 0.830142 | 0.798299 | -0.849979 | -0.845903 | -0.842329 | -0.843831 | -0.850509 | 0.814169 | 0.814169 | 0.814169 | 0.816256 | 0.813408 | 0.813408 | 0.813408 | 0.816256 | 0.834151 | 0.834151 | 0.834151 | 0.849919 | 0.920290 | 0.920290 | 0.920290 | 0.849919 | 0.989661 | 1.000000 | 1.000000 | 1.000000 | 0.989661 | 0.992773 | 0.976520 | 0.976520 | 0.976520 | 0.992773 |
| rwb | 0.046002 | -0.325603 | -0.298411 | 0.314825 | 0.270101 | 0.164308 | 0.122380 | 0.239683 | 0.531699 | -0.078242 | 0.001424 | 0.832935 | 0.698758 | 0.908033 | 0.860507 | 0.875960 | 0.915032 | -0.881761 | 0.808197 | 0.528117 | 0.781133 | 0.817747 | 0.577657 | 0.772177 | 0.702371 | 0.672190 | 0.769808 | 0.824830 | 0.493498 | 0.501434 | 0.463525 | 0.260861 | 0.400874 | 0.705215 | 0.196337 | 0.764014 | 0.168889 | 0.652589 | 0.734497 | 0.728475 | 0.668715 | 0.446975 | 0.585662 | 0.749758 | 0.788744 | 0.764351 | -0.873948 | -0.869498 | -0.865872 | -0.867852 | -0.874259 | 0.850656 | 0.850656 | 0.850656 | 0.863179 | 0.854643 | 0.854643 | 0.854643 | 0.863179 | 0.871561 | 0.871561 | 0.871561 | 0.892433 | 0.939707 | 0.939707 | 0.939707 | 0.892433 | 1.000000 | 0.989661 | 0.989661 | 0.989661 | 1.000000 | 0.995527 | 0.954171 | 0.954171 | 0.954171 | 0.995527 |
| lb | 0.049920 | -0.285420 | -0.264230 | 0.302586 | 0.255058 | 0.150920 | 0.115759 | 0.214493 | 0.477996 | -0.083102 | 0.001553 | 0.800295 | 0.643723 | 0.869601 | 0.814175 | 0.914383 | 0.915544 | -0.866853 | 0.764308 | 0.466539 | 0.789995 | 0.783080 | 0.521969 | 0.718024 | 0.651881 | 0.627274 | 0.744346 | 0.780885 | 0.450753 | 0.463781 | 0.411821 | 0.245538 | 0.358966 | 0.665902 | 0.209495 | 0.748729 | 0.201371 | 0.598755 | 0.752965 | 0.767959 | 0.612659 | 0.397495 | 0.537874 | 0.797458 | 0.833216 | 0.811201 | -0.859059 | -0.854817 | -0.851173 | -0.852858 | -0.859623 | 0.809747 | 0.809747 | 0.809747 | 0.817004 | 0.809238 | 0.809238 | 0.809238 | 0.817004 | 0.827104 | 0.827104 | 0.827104 | 0.850105 | 0.908444 | 0.908444 | 0.908444 | 0.850105 | 0.995527 | 0.992773 | 0.992773 | 0.992773 | 0.995527 | 1.000000 | 0.975842 | 0.975842 | 0.975842 | 1.000000 |
| lcb | 0.091531 | -0.152169 | -0.137809 | 0.287742 | 0.217449 | 0.123135 | 0.111357 | 0.161987 | 0.352840 | -0.098735 | -0.004776 | 0.684496 | 0.527674 | 0.770362 | 0.695236 | 0.966683 | 0.914284 | -0.813820 | 0.640622 | 0.341281 | 0.811381 | 0.700933 | 0.407616 | 0.586209 | 0.530464 | 0.524233 | 0.677501 | 0.675669 | 0.298822 | 0.325201 | 0.259553 | 0.222267 | 0.222051 | 0.585250 | 0.236918 | 0.685520 | 0.323689 | 0.484314 | 0.800709 | 0.829185 | 0.486627 | 0.293680 | 0.440939 | 0.868021 | 0.896043 | 0.870404 | -0.806065 | -0.802532 | -0.799097 | -0.799867 | -0.807174 | 0.713309 | 0.713309 | 0.713309 | 0.699402 | 0.698344 | 0.698344 | 0.698344 | 0.699402 | 0.716930 | 0.716930 | 0.716930 | 0.739007 | 0.825073 | 0.825073 | 0.825073 | 0.739007 | 0.954171 | 0.976520 | 0.976520 | 0.976520 | 0.954171 | 0.975842 | 1.000000 | 1.000000 | 1.000000 | 0.975842 |
| cb | 0.091531 | -0.152169 | -0.137809 | 0.287742 | 0.217449 | 0.123135 | 0.111357 | 0.161987 | 0.352840 | -0.098735 | -0.004776 | 0.684496 | 0.527674 | 0.770362 | 0.695236 | 0.966683 | 0.914284 | -0.813820 | 0.640622 | 0.341281 | 0.811381 | 0.700933 | 0.407616 | 0.586209 | 0.530464 | 0.524233 | 0.677501 | 0.675669 | 0.298822 | 0.325201 | 0.259553 | 0.222267 | 0.222051 | 0.585250 | 0.236918 | 0.685520 | 0.323689 | 0.484314 | 0.800709 | 0.829185 | 0.486627 | 0.293680 | 0.440939 | 0.868021 | 0.896043 | 0.870404 | -0.806065 | -0.802532 | -0.799097 | -0.799867 | -0.807174 | 0.713309 | 0.713309 | 0.713309 | 0.699402 | 0.698344 | 0.698344 | 0.698344 | 0.699402 | 0.716930 | 0.716930 | 0.716930 | 0.739007 | 0.825073 | 0.825073 | 0.825073 | 0.739007 | 0.954171 | 0.976520 | 0.976520 | 0.976520 | 0.954171 | 0.975842 | 1.000000 | 1.000000 | 1.000000 | 0.975842 |
| rcb | 0.091531 | -0.152169 | -0.137809 | 0.287742 | 0.217449 | 0.123135 | 0.111357 | 0.161987 | 0.352840 | -0.098735 | -0.004776 | 0.684496 | 0.527674 | 0.770362 | 0.695236 | 0.966683 | 0.914284 | -0.813820 | 0.640622 | 0.341281 | 0.811381 | 0.700933 | 0.407616 | 0.586209 | 0.530464 | 0.524233 | 0.677501 | 0.675669 | 0.298822 | 0.325201 | 0.259553 | 0.222267 | 0.222051 | 0.585250 | 0.236918 | 0.685520 | 0.323689 | 0.484314 | 0.800709 | 0.829185 | 0.486627 | 0.293680 | 0.440939 | 0.868021 | 0.896043 | 0.870404 | -0.806065 | -0.802532 | -0.799097 | -0.799867 | -0.807174 | 0.713309 | 0.713309 | 0.713309 | 0.699402 | 0.698344 | 0.698344 | 0.698344 | 0.699402 | 0.716930 | 0.716930 | 0.716930 | 0.739007 | 0.825073 | 0.825073 | 0.825073 | 0.739007 | 0.954171 | 0.976520 | 0.976520 | 0.976520 | 0.954171 | 0.975842 | 1.000000 | 1.000000 | 1.000000 | 0.975842 |
| rb | 0.049920 | -0.285420 | -0.264230 | 0.302586 | 0.255058 | 0.150920 | 0.115759 | 0.214493 | 0.477996 | -0.083102 | 0.001553 | 0.800295 | 0.643723 | 0.869601 | 0.814175 | 0.914383 | 0.915544 | -0.866853 | 0.764308 | 0.466539 | 0.789995 | 0.783080 | 0.521969 | 0.718024 | 0.651881 | 0.627274 | 0.744346 | 0.780885 | 0.450753 | 0.463781 | 0.411821 | 0.245538 | 0.358966 | 0.665902 | 0.209495 | 0.748729 | 0.201371 | 0.598755 | 0.752965 | 0.767959 | 0.612659 | 0.397495 | 0.537874 | 0.797458 | 0.833216 | 0.811201 | -0.859059 | -0.854817 | -0.851173 | -0.852858 | -0.859623 | 0.809747 | 0.809747 | 0.809747 | 0.817004 | 0.809238 | 0.809238 | 0.809238 | 0.817004 | 0.827104 | 0.827104 | 0.827104 | 0.850105 | 0.908444 | 0.908444 | 0.908444 | 0.850105 | 0.995527 | 0.992773 | 0.992773 | 0.992773 | 0.995527 | 1.000000 | 0.975842 | 0.975842 | 0.975842 | 1.000000 |
From the correlation we can see that maximum correlation the value has with international reputation and overall rating of the player. Which is logical.
Linear Regression model is used when we need to predict a continuous variable using a linear function of type $y=ax+b$. $a$ and $b$ are unknown parameters. Given pairs $(x,y)$ we create a model under the assumption that $y$ depends linearly on $x$. [4]
Below is the representation of data and its linear regression model taken from Wikipedia.
The red line is the approximation of the data and for one independednt variable $x$, the dependent variable $y$ will look like: $y=\theta_0+\theta_1*x_1$ (or $b+ax$). If we have more independent variables (called features) $x_1,x_2,...,x_n$ then $y=\theta_0+\theta_1*x_1+\theta_2*x_2+...+\theta_n*x_n$.
Here we will create a model with 78 features, which after converting categorical variables to numerical will become 114.
players_16.head().transpose()
| sofifa_id | 158023 | 20801 | 9014 | 167495 | 176580 |
|---|---|---|---|---|---|
| short_name | L. Messi | Cristiano Ronaldo | A. Robben | M. Neuer | L. Suárez |
| age | 28 | 30 | 31 | 29 | 28 |
| dob | 1987-06-24 | 1985-02-05 | 1984-01-23 | 1986-03-27 | 1987-01-24 |
| height_cm | 170 | 185 | 180 | 193 | 182 |
| weight_kg | 72 | 80 | 80 | 92 | 85 |
| nationality | Argentina | Portugal | Netherlands | Germany | Uruguay |
| club | FC Barcelona | Real Madrid | FC Bayern München | FC Bayern München | FC Barcelona |
| overall | 94 | 93 | 90 | 90 | 90 |
| potential | 95 | 93 | 90 | 90 | 90 |
| value_eur | 111000000 | 85500000 | 56000000 | 58000000 | 69000000 |
| player_positions | RW, CF | LW, LM | RM, LM, RW | GK | ST |
| preferred_foot | Left | Right | Left | Right | Right |
| international_reputation | 5 | 5 | 5 | 5 | 5 |
| weak_foot | 4 | 4 | 2 | 4 | 4 |
| skill_moves | 4 | 5 | 4 | 1 | 4 |
| work_rate | Medium/Low | High/Low | High/Low | Medium/Medium | High/Medium |
| body_type | Messi | C. Ronaldo | Normal | Normal | Normal |
| real_face | Yes | Yes | Yes | Yes | Yes |
| team_position | RW | LM | SUB | GK | ST |
| team_jersey_number | 10 | 7 | 10 | 1 | 9 |
| joined | 2004-07-01 | 2009-07-01 | 2009-08-28 | 2011-07-01 | 2014-07-11 |
| contract_valid_until | 2018 | 2018 | 2017 | 2019 | 2019 |
| pace | 92 | 92 | 92 | 0 | 83 |
| shooting | 88 | 93 | 86 | 0 | 88 |
| passing | 86 | 80 | 82 | 0 | 79 |
| dribbling | 95 | 91 | 92 | 0 | 87 |
| defending | 24 | 33 | 32 | 0 | 42 |
| physic | 62 | 78 | 64 | 0 | 79 |
| gk_speed | 0 | 0 | 0 | 60 | 0 |
| player_traits | Finesse Shot, Speed Dribbler (CPU AI Only), On... | Power Free-Kick, Flair, Long Shot Taker (CPU A... | Diver, Injury Prone, Avoids Using Weaker Foot,... | GK Long Throw, 1-on-1 Rush | Diver, Beat Offside Trap, Flair, Technical Dri... |
| attacking_crossing | 76 | 81 | 80 | 5 | 77 |
| attacking_finishing | 92 | 95 | 85 | 1 | 89 |
| attacking_heading_accuracy | 71 | 86 | 50 | 25 | 79 |
| attacking_short_passing | 87 | 80 | 84 | 54 | 82 |
| attacking_volleys | 85 | 87 | 86 | -3 | 89 |
| skill_dribbling | 96 | 93 | 93 | 7 | 86 |
| skill_curve | 89 | 88 | 87 | 3 | 86 |
| skill_fk_accuracy | 90 | 75 | 83 | -3 | 84 |
| skill_long_passing | 82 | 72 | 72 | 53 | 64 |
| skill_ball_control | 96 | 90 | 88 | 31 | 93 |
| movement_acceleration | 94 | 91 | 91 | 58 | 88 |
| movement_sprint_speed | 90 | 92 | 91 | 61 | 77 |
| movement_agility | 90 | 87 | 89 | 43 | 86 |
| movement_reactions | 90 | 94 | 91 | 88 | 91 |
| movement_balance | 95 | 61 | 91 | 35 | 60 |
| power_shot_power | 80 | 94 | 86 | 8 | 92 |
| power_jumping | 63 | 94 | 61 | 78 | 69 |
| power_stamina | 75 | 85 | 74 | 44 | 86 |
| power_strength | 58 | 79 | 65 | 83 | 76 |
| power_long_shots | 88 | 93 | 90 | 7 | 88 |
| mentality_aggression | 48 | 61 | 47 | 29 | 78 |
| mentality_interceptions | 22 | 34 | 39 | 30 | 41 |
| mentality_positioning | 88 | 95 | 89 | -1 | 94 |
| mentality_vision | 90 | 81 | 84 | 90 | 84 |
| mentality_penalties | 74 | 85 | 80 | 37 | 85 |
| defending_marking | 1 | 22 | 29 | -5 | 30 |
| defending_standing_tackle | 25 | 31 | 26 | -5 | 45 |
| defending_sliding_tackle | 22 | 23 | 26 | -3 | 38 |
| goalkeeping_diving | 6 | 7 | 10 | 82 | 27 |
| goalkeeping_handling | 11 | 11 | 8 | 89 | 25 |
| goalkeeping_kicking | 15 | 15 | 11 | 91 | 31 |
| goalkeeping_positioning | 14 | 14 | 5 | 90 | 33 |
| goalkeeping_reflexes | 8 | 11 | 15 | 86 | 37 |
| ls | 90 | 94 | 87 | 0 | 90 |
| st | 90 | 94 | 87 | 0 | 90 |
| rs | 90 | 94 | 87 | 0 | 90 |
| lw | 94 | 93 | 92 | 0 | 90 |
| lf | 94 | 94 | 91 | 0 | 91 |
| cf | 94 | 94 | 91 | 0 | 91 |
| rf | 94 | 94 | 91 | 0 | 91 |
| rw | 94 | 93 | 92 | 0 | 90 |
| lam | 94 | 91 | 91 | 0 | 89 |
| cam | 94 | 91 | 91 | 0 | 89 |
| ram | 94 | 91 | 91 | 0 | 89 |
| lm | 93 | 91 | 90 | 0 | 88 |
| lcm | 85 | 83 | 83 | 0 | 82 |
| cm | 85 | 83 | 83 | 0 | 82 |
| rcm | 85 | 83 | 83 | 0 | 82 |
| rm | 93 | 91 | 90 | 0 | 88 |
| lwb | 65 | 67 | 68 | 0 | 70 |
| ldm | 60 | 63 | 63 | 0 | 68 |
| cdm | 60 | 63 | 63 | 0 | 68 |
| rdm | 60 | 63 | 63 | 0 | 68 |
| rwb | 65 | 67 | 68 | 0 | 70 |
| lb | 60 | 63 | 62 | 0 | 67 |
| lcb | 47 | 55 | 50 | 0 | 61 |
| cb | 47 | 55 | 50 | 0 | 61 |
| rcb | 47 | 55 | 50 | 0 | 61 |
| rb | 60 | 63 | 62 | 0 | 67 |
Before submitting the dataset to any ML algorithm, we need to do some more preprocessing. Column "dob" (date of birth) can be removed since column "age" is there and they both give same information. Names of players should be removed, as well as "players_traits", "joined", "body_type", "real_face" and "contract_valid_until". These are hardly supposed to influence the value of the player. "players_positions" can also be removed since the values in the columns with the respective positions depict players capabilities in each position. Naturally, they will have higher values for the positions the player takes.
players_16=players_16.drop(["short_name","dob","player_traits", "joined", "body_type", "real_face","contract_valid_until","nationality","player_positions","team_jersey_number","club"],axis=1)
players_16.head().transpose()
| sofifa_id | 158023 | 20801 | 9014 | 167495 | 176580 |
|---|---|---|---|---|---|
| age | 28 | 30 | 31 | 29 | 28 |
| height_cm | 170 | 185 | 180 | 193 | 182 |
| weight_kg | 72 | 80 | 80 | 92 | 85 |
| overall | 94 | 93 | 90 | 90 | 90 |
| potential | 95 | 93 | 90 | 90 | 90 |
| value_eur | 111000000 | 85500000 | 56000000 | 58000000 | 69000000 |
| preferred_foot | Left | Right | Left | Right | Right |
| international_reputation | 5 | 5 | 5 | 5 | 5 |
| weak_foot | 4 | 4 | 2 | 4 | 4 |
| skill_moves | 4 | 5 | 4 | 1 | 4 |
| work_rate | Medium/Low | High/Low | High/Low | Medium/Medium | High/Medium |
| team_position | RW | LM | SUB | GK | ST |
| pace | 92 | 92 | 92 | 0 | 83 |
| shooting | 88 | 93 | 86 | 0 | 88 |
| passing | 86 | 80 | 82 | 0 | 79 |
| dribbling | 95 | 91 | 92 | 0 | 87 |
| defending | 24 | 33 | 32 | 0 | 42 |
| physic | 62 | 78 | 64 | 0 | 79 |
| gk_speed | 0 | 0 | 0 | 60 | 0 |
| attacking_crossing | 76 | 81 | 80 | 5 | 77 |
| attacking_finishing | 92 | 95 | 85 | 1 | 89 |
| attacking_heading_accuracy | 71 | 86 | 50 | 25 | 79 |
| attacking_short_passing | 87 | 80 | 84 | 54 | 82 |
| attacking_volleys | 85 | 87 | 86 | -3 | 89 |
| skill_dribbling | 96 | 93 | 93 | 7 | 86 |
| skill_curve | 89 | 88 | 87 | 3 | 86 |
| skill_fk_accuracy | 90 | 75 | 83 | -3 | 84 |
| skill_long_passing | 82 | 72 | 72 | 53 | 64 |
| skill_ball_control | 96 | 90 | 88 | 31 | 93 |
| movement_acceleration | 94 | 91 | 91 | 58 | 88 |
| movement_sprint_speed | 90 | 92 | 91 | 61 | 77 |
| movement_agility | 90 | 87 | 89 | 43 | 86 |
| movement_reactions | 90 | 94 | 91 | 88 | 91 |
| movement_balance | 95 | 61 | 91 | 35 | 60 |
| power_shot_power | 80 | 94 | 86 | 8 | 92 |
| power_jumping | 63 | 94 | 61 | 78 | 69 |
| power_stamina | 75 | 85 | 74 | 44 | 86 |
| power_strength | 58 | 79 | 65 | 83 | 76 |
| power_long_shots | 88 | 93 | 90 | 7 | 88 |
| mentality_aggression | 48 | 61 | 47 | 29 | 78 |
| mentality_interceptions | 22 | 34 | 39 | 30 | 41 |
| mentality_positioning | 88 | 95 | 89 | -1 | 94 |
| mentality_vision | 90 | 81 | 84 | 90 | 84 |
| mentality_penalties | 74 | 85 | 80 | 37 | 85 |
| defending_marking | 1 | 22 | 29 | -5 | 30 |
| defending_standing_tackle | 25 | 31 | 26 | -5 | 45 |
| defending_sliding_tackle | 22 | 23 | 26 | -3 | 38 |
| goalkeeping_diving | 6 | 7 | 10 | 82 | 27 |
| goalkeeping_handling | 11 | 11 | 8 | 89 | 25 |
| goalkeeping_kicking | 15 | 15 | 11 | 91 | 31 |
| goalkeeping_positioning | 14 | 14 | 5 | 90 | 33 |
| goalkeeping_reflexes | 8 | 11 | 15 | 86 | 37 |
| ls | 90 | 94 | 87 | 0 | 90 |
| st | 90 | 94 | 87 | 0 | 90 |
| rs | 90 | 94 | 87 | 0 | 90 |
| lw | 94 | 93 | 92 | 0 | 90 |
| lf | 94 | 94 | 91 | 0 | 91 |
| cf | 94 | 94 | 91 | 0 | 91 |
| rf | 94 | 94 | 91 | 0 | 91 |
| rw | 94 | 93 | 92 | 0 | 90 |
| lam | 94 | 91 | 91 | 0 | 89 |
| cam | 94 | 91 | 91 | 0 | 89 |
| ram | 94 | 91 | 91 | 0 | 89 |
| lm | 93 | 91 | 90 | 0 | 88 |
| lcm | 85 | 83 | 83 | 0 | 82 |
| cm | 85 | 83 | 83 | 0 | 82 |
| rcm | 85 | 83 | 83 | 0 | 82 |
| rm | 93 | 91 | 90 | 0 | 88 |
| lwb | 65 | 67 | 68 | 0 | 70 |
| ldm | 60 | 63 | 63 | 0 | 68 |
| cdm | 60 | 63 | 63 | 0 | 68 |
| rdm | 60 | 63 | 63 | 0 | 68 |
| rwb | 65 | 67 | 68 | 0 | 70 |
| lb | 60 | 63 | 62 | 0 | 67 |
| lcb | 47 | 55 | 50 | 0 | 61 |
| cb | 47 | 55 | 50 | 0 | 61 |
| rcb | 47 | 55 | 50 | 0 | 61 |
| rb | 60 | 63 | 62 | 0 | 67 |
We will rearrange columns little bit by putting the target attribute at the beginning of the set so we can easily remove it later.
players_16.columns
Index(['age', 'height_cm', 'weight_kg', 'overall', 'potential', 'value_eur',
'preferred_foot', 'international_reputation', 'weak_foot',
'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
'attacking_crossing', 'attacking_finishing',
'attacking_heading_accuracy', 'attacking_short_passing',
'attacking_volleys', 'skill_dribbling', 'skill_curve',
'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
'movement_reactions', 'movement_balance', 'power_shot_power',
'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
'mentality_aggression', 'mentality_interceptions',
'mentality_positioning', 'mentality_vision', 'mentality_penalties',
'defending_marking', 'defending_standing_tackle',
'defending_sliding_tackle', 'goalkeeping_diving',
'goalkeeping_handling', 'goalkeeping_kicking',
'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
'rcb', 'rb'],
dtype='object')
players_16=players_16[['value_eur','age', 'height_cm', 'weight_kg', 'overall', 'potential',
'preferred_foot', 'international_reputation', 'weak_foot',
'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
'attacking_crossing', 'attacking_finishing',
'attacking_heading_accuracy', 'attacking_short_passing',
'attacking_volleys', 'skill_dribbling', 'skill_curve',
'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
'movement_reactions', 'movement_balance', 'power_shot_power',
'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
'mentality_aggression', 'mentality_interceptions',
'mentality_positioning', 'mentality_vision', 'mentality_penalties',
'defending_marking', 'defending_standing_tackle',
'defending_sliding_tackle', 'goalkeeping_diving',
'goalkeeping_handling', 'goalkeeping_kicking',
'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
'rcb', 'rb']]
players_16.head()
| value_eur | age | height_cm | weight_kg | overall | potential | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | team_position | pace | shooting | passing | dribbling | defending | physic | gk_speed | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 158023 | 111000000 | 28 | 170 | 72 | 94 | 95 | Left | 5 | 4 | 4 | Medium/Low | RW | 92.0 | 88.0 | 86.0 | 95.0 | 24.0 | 62.0 | 0.0 | 76 | 92 | 71 | 87 | 85 | 96 | 89 | 90 | 82 | 96 | 94 | 90 | 90 | 90 | 95 | 80 | 63 | 75 | 58 | 88 | 48 | 22 | 88 | 90 | 74 | 1 | 25 | 22 | 6 | 11 | 15 | 14 | 8 | 90 | 90 | 90 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 93 | 85 | 85 | 85 | 93 | 65 | 60 | 60 | 60 | 65 | 60 | 47 | 47 | 47 | 60 |
| 20801 | 85500000 | 30 | 185 | 80 | 93 | 93 | Right | 5 | 4 | 5 | High/Low | LM | 92.0 | 93.0 | 80.0 | 91.0 | 33.0 | 78.0 | 0.0 | 81 | 95 | 86 | 80 | 87 | 93 | 88 | 75 | 72 | 90 | 91 | 92 | 87 | 94 | 61 | 94 | 94 | 85 | 79 | 93 | 61 | 34 | 95 | 81 | 85 | 22 | 31 | 23 | 7 | 11 | 15 | 14 | 11 | 94 | 94 | 94 | 93 | 94 | 94 | 94 | 93 | 91 | 91 | 91 | 91 | 83 | 83 | 83 | 91 | 67 | 63 | 63 | 63 | 67 | 63 | 55 | 55 | 55 | 63 |
| 9014 | 56000000 | 31 | 180 | 80 | 90 | 90 | Left | 5 | 2 | 4 | High/Low | SUB | 92.0 | 86.0 | 82.0 | 92.0 | 32.0 | 64.0 | 0.0 | 80 | 85 | 50 | 84 | 86 | 93 | 87 | 83 | 72 | 88 | 91 | 91 | 89 | 91 | 91 | 86 | 61 | 74 | 65 | 90 | 47 | 39 | 89 | 84 | 80 | 29 | 26 | 26 | 10 | 8 | 11 | 5 | 15 | 87 | 87 | 87 | 92 | 91 | 91 | 91 | 92 | 91 | 91 | 91 | 90 | 83 | 83 | 83 | 90 | 68 | 63 | 63 | 63 | 68 | 62 | 50 | 50 | 50 | 62 |
| 167495 | 58000000 | 29 | 193 | 92 | 90 | 90 | Right | 5 | 4 | 1 | Medium/Medium | GK | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.0 | 5 | 1 | 25 | 54 | -3 | 7 | 3 | -3 | 53 | 31 | 58 | 61 | 43 | 88 | 35 | 8 | 78 | 44 | 83 | 7 | 29 | 30 | -1 | 90 | 37 | -5 | -5 | -3 | 82 | 89 | 91 | 90 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 176580 | 69000000 | 28 | 182 | 85 | 90 | 90 | Right | 5 | 4 | 4 | High/Medium | ST | 83.0 | 88.0 | 79.0 | 87.0 | 42.0 | 79.0 | 0.0 | 77 | 89 | 79 | 82 | 89 | 86 | 86 | 84 | 64 | 93 | 88 | 77 | 86 | 91 | 60 | 92 | 69 | 86 | 76 | 88 | 78 | 41 | 94 | 84 | 85 | 30 | 45 | 38 | 27 | 25 | 31 | 33 | 37 | 90 | 90 | 90 | 90 | 91 | 91 | 91 | 90 | 89 | 89 | 89 | 88 | 82 | 82 | 82 | 88 | 70 | 68 | 68 | 68 | 70 | 67 | 61 | 61 | 61 | 67 |
players_16.shape
(14881, 78)
We are going to apply Linear Regression in order to train a model to predict players' price. scikit-learn will be used. There are few steps before that.
ML algorithms cannot work with categorical features, that's why they have to be converted to numerical. One way is by encoding (substituting) each value of a categorical feature by a binary number with n digits (actually n-1), where n is the number of unique values of the feature.
dummy_features_foot=pd.get_dummies(players_16["preferred_foot"])
dummy_features_foot.columns
Index(['Left', 'Right'], dtype='object')
dummy_features_position=pd.get_dummies(players_16["team_position"])
dummy_features_position.columns
Index(['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB'],
dtype='object')
dummy_features_rate=pd.get_dummies(players_16["work_rate"])
dummy_features_rate.columns
Index(['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium'],
dtype='object')
players_16=players_16.drop(["preferred_foot","team_position","work_rate"],axis=1)
players_16[['Left', 'Right']]=dummy_features_foot
players_16[['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB']]=dummy_features_position
players_16[['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium']]=dummy_features_rate
players_16.head()
| value_eur | age | height_cm | weight_kg | overall | potential | international_reputation | weak_foot | skill_moves | pace | shooting | passing | dribbling | defending | physic | gk_speed | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | Left | Right | CAM | CB | CDM | CF | CM | GK | LAM | LB | LCB | LCM | LDM | LF | LM | LS | LW | LWB | RAM | RB | RCB | RCM | RDM | RES | RF | RM | RS | RW | RWB | ST | SUB | High/High | High/Low | High/Medium | Low/High | Low/Low | Low/Medium | Medium/High | Medium/Low | Medium/Medium | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 158023 | 111000000 | 28 | 170 | 72 | 94 | 95 | 5 | 4 | 4 | 92.0 | 88.0 | 86.0 | 95.0 | 24.0 | 62.0 | 0.0 | 76 | 92 | 71 | 87 | 85 | 96 | 89 | 90 | 82 | 96 | 94 | 90 | 90 | 90 | 95 | 80 | 63 | 75 | 58 | 88 | 48 | 22 | 88 | 90 | 74 | 1 | 25 | 22 | 6 | 11 | 15 | 14 | 8 | 90 | 90 | 90 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 94 | 93 | 85 | 85 | 85 | 93 | 65 | 60 | 60 | 60 | 65 | 60 | 47 | 47 | 47 | 60 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 20801 | 85500000 | 30 | 185 | 80 | 93 | 93 | 5 | 4 | 5 | 92.0 | 93.0 | 80.0 | 91.0 | 33.0 | 78.0 | 0.0 | 81 | 95 | 86 | 80 | 87 | 93 | 88 | 75 | 72 | 90 | 91 | 92 | 87 | 94 | 61 | 94 | 94 | 85 | 79 | 93 | 61 | 34 | 95 | 81 | 85 | 22 | 31 | 23 | 7 | 11 | 15 | 14 | 11 | 94 | 94 | 94 | 93 | 94 | 94 | 94 | 93 | 91 | 91 | 91 | 91 | 83 | 83 | 83 | 91 | 67 | 63 | 63 | 63 | 67 | 63 | 55 | 55 | 55 | 63 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9014 | 56000000 | 31 | 180 | 80 | 90 | 90 | 5 | 2 | 4 | 92.0 | 86.0 | 82.0 | 92.0 | 32.0 | 64.0 | 0.0 | 80 | 85 | 50 | 84 | 86 | 93 | 87 | 83 | 72 | 88 | 91 | 91 | 89 | 91 | 91 | 86 | 61 | 74 | 65 | 90 | 47 | 39 | 89 | 84 | 80 | 29 | 26 | 26 | 10 | 8 | 11 | 5 | 15 | 87 | 87 | 87 | 92 | 91 | 91 | 91 | 92 | 91 | 91 | 91 | 90 | 83 | 83 | 83 | 90 | 68 | 63 | 63 | 63 | 68 | 62 | 50 | 50 | 50 | 62 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 167495 | 58000000 | 29 | 193 | 92 | 90 | 90 | 5 | 4 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.0 | 5 | 1 | 25 | 54 | -3 | 7 | 3 | -3 | 53 | 31 | 58 | 61 | 43 | 88 | 35 | 8 | 78 | 44 | 83 | 7 | 29 | 30 | -1 | 90 | 37 | -5 | -5 | -3 | 82 | 89 | 91 | 90 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 176580 | 69000000 | 28 | 182 | 85 | 90 | 90 | 5 | 4 | 4 | 83.0 | 88.0 | 79.0 | 87.0 | 42.0 | 79.0 | 0.0 | 77 | 89 | 79 | 82 | 89 | 86 | 86 | 84 | 64 | 93 | 88 | 77 | 86 | 91 | 60 | 92 | 69 | 86 | 76 | 88 | 78 | 41 | 94 | 84 | 85 | 30 | 45 | 38 | 27 | 25 | 31 | 33 | 37 | 90 | 90 | 90 | 90 | 91 | 91 | 91 | 90 | 89 | 89 | 89 | 88 | 82 | 82 | 82 | 88 | 70 | 68 | 68 | 68 | 70 | 67 | 61 | 61 | 61 | 67 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
modelling_data_features = players_16.drop("value_eur", axis = 1)
modelling_data_target = players_16["value_eur"]
modelling_data_features.columns
Index(['age', 'height_cm', 'weight_kg', 'overall', 'potential',
'international_reputation', 'weak_foot', 'skill_moves', 'pace',
'shooting',
...
'SUB', 'High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium'],
dtype='object', length=114)
In order to have better accuracy of the model, the values of all features should have same scale.
scaler = MinMaxScaler()
modelling_data_features_scaled=scaler.fit_transform(modelling_data_features)
features_train, features_test, target_train, target_test = train_test_split(
modelling_data_features_scaled, modelling_data_target, train_size = 0.7, test_size = 0.3, random_state = 42)
print(features_train.shape, features_test.shape, target_train.shape, target_test.shape, sep = "\r\n")
(10416, 114) (4465, 114) (10416,) (4465,)
model = LinearRegression()
model.fit(features_train, target_train)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)
score=model.score(features_test,target_test)
score
0.6273786112802693
players_20=pd.read_csv("data/players_20.csv",index_col="sofifa_id")
players_20.head()
| player_url | short_name | long_name | age | dob | height_cm | weight_kg | nationality | club | overall | potential | value_eur | wage_eur | player_positions | preferred_foot | international_reputation | weak_foot | skill_moves | work_rate | body_type | real_face | release_clause_eur | player_tags | team_position | team_jersey_number | loaned_from | joined | contract_valid_until | nation_position | nation_jersey_number | pace | shooting | passing | dribbling | defending | physic | gk_diving | gk_handling | gk_kicking | gk_reflexes | gk_speed | gk_positioning | player_traits | attacking_crossing | attacking_finishing | attacking_heading_accuracy | attacking_short_passing | attacking_volleys | skill_dribbling | skill_curve | skill_fk_accuracy | skill_long_passing | skill_ball_control | movement_acceleration | movement_sprint_speed | movement_agility | movement_reactions | movement_balance | power_shot_power | power_jumping | power_stamina | power_strength | power_long_shots | mentality_aggression | mentality_interceptions | mentality_positioning | mentality_vision | mentality_penalties | mentality_composure | defending_marking | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | ls | st | rs | lw | lf | cf | rf | rw | lam | cam | ram | lm | lcm | cm | rcm | rm | lwb | ldm | cdm | rdm | rwb | lb | lcb | cb | rcb | rb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sofifa_id | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 158023 | https://sofifa.com/player/158023/lionel-messi/... | L. Messi | Lionel Andrés Messi Cuccittini | 32 | 1987-06-24 | 170 | 72 | Argentina | FC Barcelona | 94 | 94 | 95500000 | 565000 | RW, CF, ST | Left | 5 | 4 | 4 | Medium/Low | Messi | Yes | 195800000.0 | #Dribbler, #Distance Shooter, #Crosser, #FK Sp... | RW | 10.0 | NaN | 2004-07-01 | 2021.0 | NaN | NaN | 87.0 | 92.0 | 92.0 | 96.0 | 39.0 | 66.0 | NaN | NaN | NaN | NaN | NaN | NaN | Beat Offside Trap, Argues with Officials, Earl... | 88 | 95 | 70 | 92 | 88 | 97 | 93 | 94 | 92 | 96 | 91 | 84 | 93 | 95 | 95 | 86 | 68 | 75 | 68 | 94 | 48 | 40 | 94 | 94 | 75 | 96 | 33 | 37 | 26 | 6 | 11 | 15 | 14 | 8 | 89+2 | 89+2 | 89+2 | 93+2 | 93+2 | 93+2 | 93+2 | 93+2 | 93+2 | 93+2 | 93+2 | 92+2 | 87+2 | 87+2 | 87+2 | 92+2 | 68+2 | 66+2 | 66+2 | 66+2 | 68+2 | 63+2 | 52+2 | 52+2 | 52+2 | 63+2 |
| 20801 | https://sofifa.com/player/20801/c-ronaldo-dos-... | Cristiano Ronaldo | Cristiano Ronaldo dos Santos Aveiro | 34 | 1985-02-05 | 187 | 83 | Portugal | Juventus | 93 | 93 | 58500000 | 405000 | ST, LW | Right | 5 | 4 | 5 | High/Low | C. Ronaldo | Yes | 96500000.0 | #Speedster, #Dribbler, #Distance Shooter, #Acr... | LW | 7.0 | NaN | 2018-07-10 | 2022.0 | LS | 7.0 | 90.0 | 93.0 | 82.0 | 89.0 | 35.0 | 78.0 | NaN | NaN | NaN | NaN | NaN | NaN | Long Throw-in, Selfish, Argues with Officials,... | 84 | 94 | 89 | 83 | 87 | 89 | 81 | 76 | 77 | 92 | 89 | 91 | 87 | 96 | 71 | 95 | 95 | 85 | 78 | 93 | 63 | 29 | 95 | 82 | 85 | 95 | 28 | 32 | 24 | 7 | 11 | 15 | 14 | 11 | 91+3 | 91+3 | 91+3 | 89+3 | 90+3 | 90+3 | 90+3 | 89+3 | 88+3 | 88+3 | 88+3 | 88+3 | 81+3 | 81+3 | 81+3 | 88+3 | 65+3 | 61+3 | 61+3 | 61+3 | 65+3 | 61+3 | 53+3 | 53+3 | 53+3 | 61+3 |
| 190871 | https://sofifa.com/player/190871/neymar-da-sil... | Neymar Jr | Neymar da Silva Santos Junior | 27 | 1992-02-05 | 175 | 68 | Brazil | Paris Saint-Germain | 92 | 92 | 105500000 | 290000 | LW, CAM | Right | 5 | 5 | 5 | High/Medium | Neymar | Yes | 195200000.0 | #Speedster, #Dribbler, #Playmaker , #Crosser,... | CAM | 10.0 | NaN | 2017-08-03 | 2022.0 | LW | 10.0 | 91.0 | 85.0 | 87.0 | 95.0 | 32.0 | 58.0 | NaN | NaN | NaN | NaN | NaN | NaN | Power Free-Kick, Injury Free, Selfish, Early C... | 87 | 87 | 62 | 87 | 87 | 96 | 88 | 87 | 81 | 95 | 94 | 89 | 96 | 92 | 84 | 80 | 61 | 81 | 49 | 84 | 51 | 36 | 87 | 90 | 90 | 94 | 27 | 26 | 29 | 9 | 9 | 15 | 15 | 11 | 84+3 | 84+3 | 84+3 | 90+3 | 89+3 | 89+3 | 89+3 | 90+3 | 90+3 | 90+3 | 90+3 | 89+3 | 82+3 | 82+3 | 82+3 | 89+3 | 66+3 | 61+3 | 61+3 | 61+3 | 66+3 | 61+3 | 46+3 | 46+3 | 46+3 | 61+3 |
| 200389 | https://sofifa.com/player/200389/jan-oblak/20/... | J. Oblak | Jan Oblak | 26 | 1993-01-07 | 188 | 87 | Slovenia | Atlético Madrid | 91 | 93 | 77500000 | 125000 | GK | Right | 3 | 3 | 1 | Medium/Medium | Normal | Yes | 164700000.0 | NaN | GK | 13.0 | NaN | 2014-07-16 | 2023.0 | GK | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | 87.0 | 92.0 | 78.0 | 89.0 | 52.0 | 90.0 | Flair, Acrobatic Clearance | 13 | 11 | 15 | 43 | 13 | 12 | 13 | 14 | 40 | 30 | 43 | 60 | 67 | 88 | 49 | 59 | 78 | 41 | 78 | 12 | 34 | 19 | 11 | 65 | 11 | 68 | 27 | 12 | 18 | 87 | 92 | 78 | 90 | 89 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 183277 | https://sofifa.com/player/183277/eden-hazard/2... | E. Hazard | Eden Hazard | 28 | 1991-01-07 | 175 | 74 | Belgium | Real Madrid | 91 | 91 | 90000000 | 470000 | LW, CF | Right | 4 | 4 | 4 | High/Medium | Normal | Yes | 184500000.0 | #Speedster, #Dribbler, #Acrobat | LW | 7.0 | NaN | 2019-07-01 | 2024.0 | LF | 10.0 | 91.0 | 83.0 | 86.0 | 94.0 | 35.0 | 66.0 | NaN | NaN | NaN | NaN | NaN | NaN | Beat Offside Trap, Selfish, Finesse Shot, Spee... | 81 | 84 | 61 | 89 | 83 | 95 | 83 | 79 | 83 | 94 | 94 | 88 | 95 | 90 | 94 | 82 | 56 | 84 | 63 | 80 | 54 | 41 | 87 | 89 | 88 | 91 | 34 | 27 | 22 | 11 | 12 | 6 | 8 | 8 | 83+3 | 83+3 | 83+3 | 89+3 | 88+3 | 88+3 | 88+3 | 89+3 | 89+3 | 89+3 | 89+3 | 89+3 | 83+3 | 83+3 | 83+3 | 89+3 | 66+3 | 63+3 | 63+3 | 63+3 | 66+3 | 61+3 | 49+3 | 49+3 | 49+3 | 61+3 |
The new dataset has same format as the old one, so we will do the same steps for data cleaning.
players_20_for_modelling=players_20.drop(["player_url","long_name","wage_eur","release_clause_eur","mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags"],axis=1)
players_20_for_modelling=players_20_for_modelling.drop(["gk_diving","gk_handling", "gk_kicking","gk_reflexes","gk_positioning"],axis=1)
cols_for_fill_20=['pace',
'shooting', 'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
'attacking_crossing',
'attacking_finishing', 'attacking_heading_accuracy',
'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
'movement_agility', 'movement_reactions', 'movement_balance',
'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
'mentality_positioning', 'mentality_vision', 'mentality_penalties',
'defending_marking', 'defending_standing_tackle',
'defending_sliding_tackle', 'goalkeeping_diving',
'goalkeeping_handling', 'goalkeeping_kicking',
'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
'rcb', 'rb']
transform_columns_to_number(players_20_for_modelling,cols_for_fill_20)
players_20_for_modelling.isnull().sum().sort_values(ascending=False)
player_traits 10712 joined 1288 contract_valid_until 240 team_jersey_number 240 team_position 240 attacking_finishing 0 dribbling 0 defending 0 physic 0 gk_speed 0 attacking_crossing 0 attacking_heading_accuracy 0 shooting 0 attacking_short_passing 0 attacking_volleys 0 skill_dribbling 0 skill_curve 0 skill_fk_accuracy 0 skill_long_passing 0 skill_ball_control 0 movement_acceleration 0 movement_sprint_speed 0 passing 0 rb 0 pace 0 movement_reactions 0 age 0 dob 0 height_cm 0 weight_kg 0 nationality 0 club 0 overall 0 potential 0 value_eur 0 player_positions 0 preferred_foot 0 international_reputation 0 weak_foot 0 skill_moves 0 work_rate 0 body_type 0 real_face 0 movement_agility 0 movement_balance 0 rcb 0 power_shot_power 0 cf 0 rf 0 rw 0 lam 0 cam 0 ram 0 lm 0 lcm 0 cm 0 rcm 0 rm 0 lwb 0 ldm 0 cdm 0 rdm 0 rwb 0 lb 0 lcb 0 cb 0 lf 0 lw 0 rs 0 mentality_penalties 0 power_jumping 0 power_stamina 0 power_strength 0 power_long_shots 0 mentality_aggression 0 mentality_interceptions 0 mentality_positioning 0 mentality_vision 0 defending_marking 0 st 0 defending_standing_tackle 0 defending_sliding_tackle 0 goalkeeping_diving 0 goalkeeping_handling 0 goalkeeping_kicking 0 goalkeeping_positioning 0 goalkeeping_reflexes 0 ls 0 short_name 0 dtype: int64
columns_20=["joined","team_position","contract_valid_until"]
fill_missing_data(players_20_for_modelling,columns)
players_20_for_modelling["team_jersey_number"]=players_20_for_modelling["team_jersey_number"].fillna(0)
players_20_for_modelling.isnull().sum().sort_values(ascending=False)
player_traits 10712 rb 0 movement_reactions 0 shooting 0 passing 0 dribbling 0 defending 0 physic 0 gk_speed 0 attacking_crossing 0 attacking_finishing 0 attacking_heading_accuracy 0 attacking_short_passing 0 attacking_volleys 0 skill_dribbling 0 skill_curve 0 skill_fk_accuracy 0 skill_long_passing 0 skill_ball_control 0 movement_acceleration 0 movement_sprint_speed 0 pace 0 contract_valid_until 0 joined 0 value_eur 0 age 0 dob 0 height_cm 0 weight_kg 0 nationality 0 club 0 overall 0 potential 0 player_positions 0 team_jersey_number 0 preferred_foot 0 international_reputation 0 weak_foot 0 skill_moves 0 work_rate 0 body_type 0 real_face 0 team_position 0 movement_agility 0 movement_balance 0 rcb 0 power_shot_power 0 cf 0 rf 0 rw 0 lam 0 cam 0 ram 0 lm 0 lcm 0 cm 0 rcm 0 rm 0 lwb 0 ldm 0 cdm 0 rdm 0 rwb 0 lb 0 lcb 0 cb 0 lf 0 lw 0 rs 0 mentality_penalties 0 power_jumping 0 power_stamina 0 power_strength 0 power_long_shots 0 mentality_aggression 0 mentality_interceptions 0 mentality_positioning 0 mentality_vision 0 defending_marking 0 st 0 defending_standing_tackle 0 defending_sliding_tackle 0 goalkeeping_diving 0 goalkeeping_handling 0 goalkeeping_kicking 0 goalkeeping_positioning 0 goalkeeping_reflexes 0 ls 0 short_name 0 dtype: int64
players_20_for_modelling=players_20_for_modelling.drop(["short_name","dob","player_traits", "joined", "body_type", "real_face","contract_valid_until","player_positions","nationality","team_jersey_number","club"],axis=1)
players_20_for_modelling=players_20_for_modelling[['value_eur','age', 'height_cm', 'weight_kg', 'overall', 'potential',
'preferred_foot', 'international_reputation', 'weak_foot',
'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
'attacking_crossing', 'attacking_finishing',
'attacking_heading_accuracy', 'attacking_short_passing',
'attacking_volleys', 'skill_dribbling', 'skill_curve',
'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
'movement_reactions', 'movement_balance', 'power_shot_power',
'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
'mentality_aggression', 'mentality_interceptions',
'mentality_positioning', 'mentality_vision', 'mentality_penalties',
'defending_marking', 'defending_standing_tackle',
'defending_sliding_tackle', 'goalkeeping_diving',
'goalkeeping_handling', 'goalkeeping_kicking',
'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
'rcb', 'rb']]
dummy_features_foot_20=pd.get_dummies(players_20_for_modelling["preferred_foot"])
dummy_features_position_20=pd.get_dummies(players_20_for_modelling["team_position"])
dummy_features_rate_20=pd.get_dummies(players_20_for_modelling["work_rate"])
players_20_for_modelling=players_20_for_modelling.drop(["preferred_foot","team_position","work_rate"],axis=1)
players_20_for_modelling[['Left', 'Right']]=dummy_features_foot_20
players_20_for_modelling[['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB']]=dummy_features_position_20
players_20_for_modelling[['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium']]=dummy_features_rate_20
modelling_data_20=pd.get_dummies(players_20_for_modelling)
modelling_data_features_20 = modelling_data_20.drop("value_eur", axis = 1)
modelling_data_target_20 = modelling_data_20["value_eur"]
scaler = MinMaxScaler()
modelling_data_features_scaled_20=scaler.fit_transform(modelling_data_features_20)
score=model.score(modelling_data_features_scaled_20,modelling_data_target_20 )
score
0.5364270009269339
This is very low accuracy. Either data is not properly preprocessed, or the available data has not much relation to the price of players. Main features that influence value might not be present in the data.
english_players_price_20=players_20[players_20["nationality"]=="England"]["value_eur"]
spanish_players_price_20=players_20[players_20["nationality"]=="Spain"]["value_eur"]
ttest_ind(english_players_price_20, spanish_players_price_20)
Ttest_indResult(statistic=-12.978089447519524, pvalue=2.0915163608115403e-37)
Here the pvalue is even smaller than for 2016, which again leads to rejecting the H0 hypothesis. All this means that Spanish and English players do not have equal payment.
Many more analysis can be done with this dataset. Players_traits can be investigated. Do they have any influence on the value? Individual players progress through the years can be also checked (the complete set contains data for all years 2015 - 2020).
[1] Wikipedia
[2] https://www.guidetofootball.com/tactics/playing-positions/
[3] Yordan Darakchiev. SoftUni "Data Science" course, Working-with-Spatial-Data-and-Network-Analysis, 2020.
[4] Yordan Darakchiev. SoftUni "Data Science" course, Regression Models, 2020.
[5] Jake VanderPlas. Python Data Science Handbook. O'Reilly, 2017.